Source: DESCRIPTION
Palate J, Quartier-la-Tente A (2023). +
Palate J, Quartier-la-Tente A (2024). rjd3x13: Seasonal Adjustment with X-13 and 'JDemetra+ 3.0'. R package version 3.2.2, https://rjdemetra.github.io/rjd3x13/, https://github.com/rjdemetra/rjd3x13.
@Manual{,
title = {rjd3x13: Seasonal Adjustment with X-13 and 'JDemetra+ 3.0'},
author = {Jean Palate and Alain Quartier-la-Tente},
- year = {2023},
+ year = {2024},
note = {R package version 3.2.2, https://rjdemetra.github.io/rjd3x13/},
url = {https://github.com/rjdemetra/rjd3x13},
}
diff --git a/pkgdown.yml b/pkgdown.yml
index d73fd58..61266d0 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -2,7 +2,7 @@ pandoc: 2.19.2
pkgdown: 2.0.7
pkgdown_sha: ~
articles: {}
-last_built: 2023-12-12T08:57Z
+last_built: 2024-01-08T11:23Z
urls:
reference: https://rjdemetra.github.io/rjd3x13/reference
article: https://rjdemetra.github.io/rjd3x13/articles
diff --git a/search.json b/search.json
index 851dae4..8889818 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://rjdemetra.github.io/rjd3x13/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Jean Palate. Author, maintainer. Alain Quartier-la-Tente. Author.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Palate J, Quartier-la-Tente (2023). rjd3x13: Seasonal Adjustment X-13 'JDemetra+ 3.0'. R package version 3.2.2, https://rjdemetra.github.io/rjd3x13/, https://github.com/rjdemetra/rjd3x13.","code":"@Manual{, title = {rjd3x13: Seasonal Adjustment with X-13 and 'JDemetra+ 3.0'}, author = {Jean Palate and Alain Quartier-la-Tente}, year = {2023}, note = {R package version 3.2.2, https://rjdemetra.github.io/rjd3x13/}, url = {https://github.com/rjdemetra/rjd3x13}, }"},{"path":"https://rjdemetra.github.io/rjd3x13/index.html","id":"rjd3x13","dir":"","previous_headings":"","what":"Seasonal Adjustment with X-13 and JDemetra+ 3.0","title":"Seasonal Adjustment with X-13 and JDemetra+ 3.0","text":"rjd3x13 offers full acces options outputs X-13-Arima (rjd3x13::x13()), including RegARIMA modelling (rjd3x13::regarima()) X-11 decomposition (rjd3x13::x11()). specification can created functions rjd3x13::regarima_spec(), rjd3x13::x11_spec() rjd3x13::x13_spec() can modified function: pre-processing: rjd3toolkit::set_arima(), rjd3toolkit::set_automodel(), rjd3toolkit::set_basic(), rjd3toolkit::set_easter(), rjd3toolkit::set_estimate(), rjd3toolkit::set_outlier(), rjd3toolkit::set_tradingdays(), rjd3toolkit::set_transform(), rjd3toolkit::add_outlier(), rjd3toolkit::remove_outlier(), rjd3toolkit::add_ramp(), rjd3toolkit::remove_ramp(), rjd3toolkit::add_usrdefvar(); decomposition: rjd3x13::set_x11(); benchmarking: rjd3toolkit::set_benchmarking().","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Seasonal Adjustment with X-13 and JDemetra+ 3.0","text":"get current stable version (latest release): get current development version GitHub:","code":"# install.packages(\"remotes\") remotes::install_github(\"rjdemetra/rjd3x13@*release\") # install.packages(\"remotes\") remotes::install_github(\"rjdemetra/rjd3x13\")"},{"path":"https://rjdemetra.github.io/rjd3x13/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Seasonal Adjustment with X-13 and JDemetra+ 3.0","text":"","code":"library(\"rjd3x13\") y <- rjd3toolkit::ABS$X0.2.09.10.M x13_model <- x13(y) summary(x13_model$result$preprocessing) # Summary of regarima model #> Log-transformation: yes #> SARIMA model: (2,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat Pr(>|t|) #> phi(1) 0.34740 0.06502 5.343 1.53e-07 *** #> phi(2) 0.21733 0.06000 3.622 0.000329 *** #> theta(1) -0.69937 0.05115 -13.672 < 2e-16 *** #> btheta(1) -0.48038 0.06993 -6.869 2.45e-11 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Regression model: #> Estimate Std. Error T-stat Pr(>|t|) #> td 0.0023233 0.0006844 3.395 0.000755 *** #> easter 0.0520113 0.0084894 6.127 2.13e-09 *** #> TC (2000-06-01) 0.1590340 0.0288578 5.511 6.37e-08 *** #> AO (2000-07-01) -0.2900774 0.0400551 -7.242 2.25e-12 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> Number of observations: 425 , Number of effective observations: 412 , Number of parameters: 9 #> Loglikelihood: 746.7517, Adjusted loglikelihood: -2120.875 #> Standard error of the regression (ML estimate): 0.03927991 #> AIC: 4259.75 , AICc: 4260.198 , BIC: 4295.939 plot(x13_model) # Plot of the final decomposition"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/deprecated-rjd3x13.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated functions — deprecated-rjd3x13","title":"Deprecated functions — deprecated-rjd3x13","text":"Deprecated functions","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/deprecated-rjd3x13.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated functions — deprecated-rjd3x13","text":"","code":"spec_x13(name = c(\"rsa4\", \"rsa0\", \"rsa1\", \"rsa2c\", \"rsa3\", \"rsa5c\")) spec_regarima(name = c(\"rg4\", \"rg0\", \"rg1\", \"rg2c\", \"rg3\", \"rg5c\")) spec_x11() fast_x13( ts, spec = c(\"rsa4\", \"rsa0\", \"rsa1\", \"rsa2c\", \"rsa3\", \"rsa5c\"), context = NULL, userdefined = NULL ) fast_regarima( ts, spec = c(\"rg4\", \"rg0\", \"rg1\", \"rg2c\", \"rg3\", \"rg5c\"), context = NULL, userdefined = NULL )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/deprecated-rjd3x13.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deprecated functions — deprecated-rjd3x13","text":"ts, spec, context, userdefined, name Parameters.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/jd3_utilities.html","id":null,"dir":"Reference","previous_headings":"","what":"Java Utility Functions — jd3_utilities","title":"Java Utility Functions — jd3_utilities","text":"functions used JDemetra+ 3.0 packages easily interact R Java objects.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/jd3_utilities.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Java Utility Functions — jd3_utilities","text":"","code":".x13_rslts(jrslts) .jd2r_spec_x11(jspec) .r2jd_spec_x11(spec) .r2jd_spec_x13(spec) .jd2r_spec_x13(jspec)"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/jd3_utilities.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Java Utility Functions — jd3_utilities","text":"spec, jspec, jrslts parameters.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":null,"dir":"Reference","previous_headings":"","what":"Refresh a specification with constraints — refresh","title":"Refresh a specification with constraints — refresh","text":"Function allowing create new specification updating specification used previous estimation. selected parameters kept fixed (previous estimation results) others freed re-estimation domain constraints. See details examples.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Refresh a specification with constraints — refresh","text":"","code":"regarima_refresh( spec, refspec = NULL, policy = c(\"FreeParameters\", \"Complete\", \"Outliers_StochasticComponent\", \"Outliers\", \"FixedParameters\", \"FixedAutoRegressiveParameters\", \"Fixed\", \"Current\"), period = 0, start = NULL, end = NULL ) x13_refresh( spec, refspec = NULL, policy = c(\"FreeParameters\", \"Complete\", \"Outliers_StochasticComponent\", \"Outliers\", \"FixedParameters\", \"FixedAutoRegressiveParameters\", \"Fixed\", \"Current\"), period = 0, start = NULL, end = NULL )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Refresh a specification with constraints — refresh","text":"spec current specification refreshed (\"result_spec\"). refspec reference specification used define domain considered re-estimation (\"domain_spec\"). default \"RG5c\" \"RSA5\" specification. policy refresh policy apply (see details). period, start, end specify span outliers re-identified (.e.: re-detected) policy = \"Outliers\" policy = \"Outliers_StochasticComponent\". Span definition: period: numeric, number observations year (12, 4...). start end: first last date outliers re-identfied, defined arrays two elements: year first period (example, period = 12, c(1980, 1) January 1980). specified, outliers re-identified whole series.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Refresh a specification with constraints — refresh","text":"new specification, object class \"JD3_X13_SPEC\" \"JD3_REGARIMA_SPEC\".","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Refresh a specification with constraints — refresh","text":"selection constraints kept fixed re-estimated called revision policy. User-defined parameters always copied new refreshed specifications. X-13 reg-arima part can refreshed. X-11 decomposition completely re-run, keeping user-defined parameters original specification. Available refresh policies : Current: applying current pre-adjustment reg-arima model adding new raw data points Additive Outliers (defined new intervention variables) Fixed: applying current pre-adjustment reg-arima model replacing forecasts new raw data points. FixedParameters: pre-adjustment reg-arima model partially modified: regression coefficients re-estimated regression variables, Arima orders coefficients unchanged. FixedAutoRegressiveParameters: FixedParameters Arima Moving Average coefficients (MA) also re-estimated, Auto-regressive (AR) coefficients kept fixed. FreeParameters: regression Arima model coefficients re-estimated, regression variables Arima orders kept fixed. Outliers: regression variables Arima orders kept fixed, outliers re-detected defined span, thus regression Arima model coefficients re-estimated Outliers_StochasticComponent: \"Outliers\" Arima model orders (p,d,q)(P,D,Q) can also re-identified.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Refresh a specification with constraints — refresh","text":"information revision policies JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/t-rev-policies-production","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Refresh a specification with constraints — refresh","text":"","code":"y<- rjd3toolkit::ABS$X0.2.08.10.M # raw series for first estimation y_raw <-window(y,end = 2009) # raw series for second (refreshed) estimation y_new <-window(y,end = 2010) # specification for first estimation spec_x13_1<-x13_spec(\"rsa5c\") # first estimation sa_x13<- x13(y_raw, spec_x13_1) # refreshing the specification current_result_spec <- sa_x13$result_spec current_domain_spec <- sa_x13$estimation_spec spec_x13_ref <- x13_refresh(current_result_spec, # point spec to be refreshed current_domain_spec, #domain spec (set of constraints) policy = \"Fixed\") # 2nd estimation with refreshed specification sa_x13_ref <- x13(y_new, spec_x13_ref)"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima.html","id":null,"dir":"Reference","previous_headings":"","what":"RegARIMA model, pre-adjustment in X13 — regarima","title":"RegARIMA model, pre-adjustment in X13 — regarima","text":"RegARIMA model, pre-adjustment X13","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RegARIMA model, pre-adjustment in X13 — regarima","text":"","code":"regarima( ts, spec = c(\"rg4\", \"rg0\", \"rg1\", \"rg2c\", \"rg3\", \"rg5c\"), context = NULL, userdefined = NULL ) regarima_fast( ts, spec = c(\"rg4\", \"rg0\", \"rg1\", \"rg2c\", \"rg3\", \"rg5c\"), context = NULL, userdefined = NULL )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RegARIMA model, pre-adjustment in X13 — regarima","text":"ts univariate time series. spec model specification. Can either name predefined specification user-defined specification. context list external regressors (calendar ) used estimation userdefined vector containing additional output variables (see x13_dictionary()).","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RegARIMA model, pre-adjustment in X13 — regarima","text":"regarima() function returns list results (\"JD3_REGARIMA_RSLTS\" object), estimation specification result specification, regarima_fast() faster function returns results.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RegARIMA model, pre-adjustment in X13 — regarima","text":"","code":"y = rjd3toolkit::ABS$X0.2.09.10.M sp = regarima_spec(\"rg5c\") sp = rjd3toolkit::add_outlier(sp, type = c(\"AO\"), c(\"2015-01-01\", \"2010-01-01\")) regarima_fast(y, spec = sp) #> Log-transformation: yes #> SARIMA model: (0,1,1) (1,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> theta(1) -0.81606 0.06959 -11.73 #> bphi(1) -0.43734 0.02661 -16.44 #> btheta(1) -0.82509 0.04481 -18.41 #> #> Regression model: #> Estimate Std. Error T-stat #> monday -0.008747 0.003287 -2.661 #> tuesday 0.004488 0.003314 1.354 #> wednesday -0.001471 0.003294 -0.447 #> thursday 0.013886 0.003325 4.176 #> friday -0.001944 0.003325 -0.585 #> saturday 0.015368 0.003304 4.651 #> easter 0.051130 0.006621 7.723 #> AO (2010-01-01) 0.035349 0.028796 1.228 #> AO (2015-01-01) -0.020385 0.028885 -0.706 #> TC (2000-06-01) 0.162169 0.026510 6.117 #> AO (2000-07-01) -0.306536 0.032095 -9.551 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 15 #> #> Loglikelihood: 795.8429 #> Adjusted loglikelihood: -2071.784 #> #> Standard error of the regression (ML estimate): 0.03478056 #> AIC: 4173.568 #> AICC: 4174.78 #> BIC: 4233.883 #> sp = rjd3toolkit::set_transform( rjd3toolkit::set_tradingdays( rjd3toolkit::set_easter(sp, enabled = FALSE), option = \"workingdays\" ), fun = \"None\" ) regarima_fast(y, spec = sp) #> Log-transformation: no #> SARIMA model: (3,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> phi(1) 0.16557 0.06461 2.563 #> phi(2) 0.08780 0.06435 1.364 #> phi(3) -0.11287 0.06047 -1.867 #> theta(1) -0.86076 0.03927 -21.918 #> btheta(1) -0.22918 0.05193 -4.413 #> #> Regression model: #> Estimate Std. Error T-stat #> td 1.0015 0.8098 1.237 #> lp 29.6392 11.5469 2.567 #> AO (2010-01-01) 37.0476 34.4300 1.076 #> AO (2015-01-01) 27.9946 35.0234 0.799 #> AO (2000-06-01) 199.7536 34.5756 5.777 #> AO (2000-07-01) -194.6887 34.6240 -5.623 #> LS (2005-04-01) -82.3062 17.4210 -4.725 #> LS (2015-07-01) 81.3334 18.0924 4.495 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 14 #> #> Loglikelihood: -2159.902 #> Standard error of the regression (ML estimate): 45.632 #> AIC: 4347.804 #> AICC: 4348.862 #> BIC: 4404.099 #> sp = rjd3toolkit::set_outlier(sp, outliers.type = c(\"AO\")) regarima_fast(y, spec = sp) #> Log-transformation: no #> SARIMA model: (3,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> phi(1) 0.11808 0.09633 1.226 #> phi(2) 0.03364 0.09199 0.366 #> phi(3) -0.15061 0.08002 -1.882 #> theta(1) -0.83611 0.07714 -10.838 #> btheta(1) -0.24114 0.05412 -4.456 #> #> Regression model: #> Estimate Std. Error T-stat #> td 1.0012 0.8028 1.247 #> lp 30.8981 11.7820 2.622 #> AO (2010-01-01) 37.2796 35.1800 1.060 #> AO (2015-01-01) 6.5622 35.1307 0.187 #> AO (2000-06-01) 194.6157 35.1753 5.533 #> AO (2000-07-01) -201.1923 35.2286 -5.711 #> AO (2005-04-01) -150.2768 35.1047 -4.281 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 13 #> #> Loglikelihood: -2169.985 #> Standard error of the regression (ML estimate): 46.77874 #> AIC: 4365.971 #> AICC: 4366.885 #> BIC: 4418.244 #>"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Outlier Detection with a RegARIMA Model — regarima_outliers","title":"Outlier Detection with a RegARIMA Model — regarima_outliers","text":"Outlier Detection RegARIMA Model","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outlier Detection with a RegARIMA Model — regarima_outliers","text":"","code":"regarima_outliers( y, order = c(0L, 1L, 1L), seasonal = c(0L, 1L, 1L), mean = F, X = NULL, X.td = NULL, ao = T, ls = T, tc = F, so = F, cv = 0, clean = F )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outlier Detection with a RegARIMA Model — regarima_outliers","text":"y dependent variable (ts object). order, seasonal orders ARIMA model. mean Boolean include mean. X user defined regressors (calendar). X.td calendar regressors. ao, ls, , tc Boolean indicate type outliers detected. cv numeric. entered critical value outlier detection procedure. equal 0 critical value outlier detection procedure automatically determined number observations. clean Clean missing values beginning/end series. Regression variables automatically resized, need .","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Outlier Detection with a RegARIMA Model — regarima_outliers","text":"\"JD3_REGARIMA_OUTLIERS\" object, containing input variables results","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outlier Detection with a RegARIMA Model — regarima_outliers","text":"","code":"regarima_outliers(rjd3toolkit::ABS$X0.2.09.10.M) #> $model #> $model$y #> Jan Feb Mar Apr May Jun Jul Aug Sep Oct #> 1982 460.1 502.6 443.8 459.1 438.4 465.1 452.7 #> 1983 379.2 378.0 472.1 503.4 510.6 462.4 468.3 458.2 482.7 485.3 #> 1984 414.7 414.5 484.7 487.3 597.9 500.4 543.4 503.4 522.8 556.6 #> 1985 516.3 452.5 525.8 587.7 700.3 561.8 602.8 582.5 563.1 637.1 #> 1986 570.5 478.2 547.4 594.3 751.6 553.4 663.2 581.1 661.9 665.6 #> 1987 613.9 513.2 599.9 674.1 714.0 670.5 720.9 601.6 672.3 709.1 #> 1988 631.0 551.1 678.1 715.7 740.8 722.0 683.5 650.9 723.3 729.6 #> 1989 631.5 552.0 719.0 697.6 764.8 786.3 715.1 723.8 757.9 751.7 #> 1990 678.2 586.2 726.8 744.1 815.5 832.4 710.3 759.4 741.1 786.6 #> 1991 694.0 604.7 719.2 748.2 828.2 746.9 794.5 770.4 741.5 858.6 #> 1992 740.0 665.9 701.5 831.4 878.6 826.0 788.2 723.6 819.8 902.5 #> 1993 762.1 643.0 754.1 840.7 906.6 887.1 771.5 728.7 844.7 886.9 #> 1994 745.7 664.4 821.5 831.7 908.0 912.6 782.9 798.8 887.0 934.6 #> 1995 752.4 682.5 811.2 906.0 927.2 906.8 880.6 873.9 856.8 920.6 #> 1996 833.1 737.1 812.0 895.2 962.8 908.6 908.0 888.9 833.7 933.7 #> 1997 840.9 727.4 857.9 849.0 994.8 830.2 971.1 836.0 939.1 976.9 #> 1998 917.3 716.2 822.9 970.1 970.2 849.4 1042.3 869.9 939.4 1021.3 #> 1999 942.0 738.4 903.2 953.2 1011.2 894.4 1054.5 899.5 1002.3 1043.7 #> 2000 924.9 798.2 901.9 1024.7 1052.3 1165.5 859.3 1009.2 1054.6 1070.4 #> 2001 971.9 814.6 1017.5 1039.2 1123.5 1024.9 1100.8 963.0 1012.9 1132.0 #> 2002 1027.9 841.4 1043.9 1075.3 1190.9 1143.0 1075.7 1065.9 1060.1 1211.4 #> 2003 1099.3 900.5 1092.7 1222.4 1237.1 1237.9 1182.0 1101.2 1198.2 1316.1 #> 2004 1182.9 989.8 1131.4 1277.1 1280.3 1384.1 1305.9 1166.8 1317.9 1358.3 #> 2005 1246.3 1037.3 1300.8 1153.7 1264.2 1454.2 1290.1 1210.7 1277.8 1314.4 #> 2006 1193.7 1037.7 1204.5 1348.6 1267.6 1429.0 1412.0 1239.2 1219.1 1344.6 #> 2007 1267.3 1047.0 1331.6 1302.6 1365.1 1491.5 1462.3 1315.5 1353.3 1440.6 #> 2008 1397.8 1140.5 1351.7 1396.6 1421.1 1401.6 1582.3 1268.4 1383.3 1452.4 #> 2009 1451.0 1056.6 1386.9 1509.1 1519.4 1500.5 1570.7 1341.5 1399.9 1534.3 #> 2010 1469.1 1111.9 1379.9 1389.7 1427.2 1551.4 1581.0 1324.0 1422.0 1464.9 #> 2011 1412.6 1117.5 1321.6 1472.6 1408.9 1471.9 1532.5 1293.5 1345.7 1404.7 #> 2012 1362.4 1131.7 1349.2 1391.2 1456.9 1616.4 1423.4 1359.0 1367.8 1442.6 #> 2013 1397.4 1113.6 1397.3 1339.1 1441.9 1537.4 1390.6 1337.2 1359.4 1463.3 #> 2014 1451.0 1064.9 1293.2 1442.9 1411.8 1461.6 1501.6 1254.2 1356.4 1478.7 #> 2015 1471.2 1053.8 1367.2 1442.2 1428.7 1480.9 1540.9 1331.9 1400.1 1566.3 #> 2016 1519.2 1155.8 1451.5 1451.0 1449.7 1596.1 1468.3 1293.9 1393.5 1497.4 #> 2017 1428.5 1092.4 1370.3 1522.6 1452.4 1557.2 1445.5 1303.1 #> Nov Dec #> 1982 522.9 889.3 #> 1983 568.7 963.7 #> 1984 623.2 1039.4 #> 1985 697.1 1187.5 #> 1986 700.9 1367.9 #> 1987 743.2 1460.1 #> 1988 870.3 1570.0 #> 1989 923.8 1569.4 #> 1990 931.5 1563.1 #> 1991 944.7 1600.3 #> 1992 968.6 1650.9 #> 1993 970.0 1710.5 #> 1994 1000.4 1817.5 #> 1995 1067.4 1857.2 #> 1996 1081.6 1837.6 #> 1997 1111.3 1879.1 #> 1998 1137.7 1975.7 #> 1999 1207.2 2069.6 #> 2000 1232.5 2177.5 #> 2001 1344.8 2269.5 #> 2002 1495.1 2338.6 #> 2003 1528.2 2424.2 #> 2004 1536.7 2500.8 #> 2005 1540.4 2536.0 #> 2006 1623.3 2611.1 #> 2007 1687.9 2747.0 #> 2008 1675.9 2886.1 #> 2009 1736.6 2795.1 #> 2010 1705.5 2752.4 #> 2011 1660.0 2730.5 #> 2012 1672.9 2753.3 #> 2013 1668.9 2725.5 #> 2014 1687.7 2756.9 #> 2015 1730.5 2913.6 #> 2016 1684.3 2850.4 #> 2017 #> #> $model$variables #> [1] \"AO.220\" \"AO.219\" \"AO.277\" \"LS.400\" \"LS.280\" #> #> $model$X #> [,1] [,2] [,3] [,4] [,5] #> [1,] 0 0 0 -1 -1 #> [2,] 0 0 0 -1 -1 #> [3,] 0 0 0 -1 -1 #> [4,] 0 0 0 -1 -1 #> [5,] 0 0 0 -1 -1 #> [6,] 0 0 0 -1 -1 #> [7,] 0 0 0 -1 -1 #> [8,] 0 0 0 -1 -1 #> [9,] 0 0 0 -1 -1 #> [10,] 0 0 0 -1 -1 #> [11,] 0 0 0 -1 -1 #> [12,] 0 0 0 -1 -1 #> [13,] 0 0 0 -1 -1 #> [14,] 0 0 0 -1 -1 #> [15,] 0 0 0 -1 -1 #> [16,] 0 0 0 -1 -1 #> [17,] 0 0 0 -1 -1 #> [18,] 0 0 0 -1 -1 #> [19,] 0 0 0 -1 -1 #> [20,] 0 0 0 -1 -1 #> [21,] 0 0 0 -1 -1 #> [22,] 0 0 0 -1 -1 #> [23,] 0 0 0 -1 -1 #> [24,] 0 0 0 -1 -1 #> [25,] 0 0 0 -1 -1 #> [26,] 0 0 0 -1 -1 #> [27,] 0 0 0 -1 -1 #> [28,] 0 0 0 -1 -1 #> [29,] 0 0 0 -1 -1 #> [30,] 0 0 0 -1 -1 #> [31,] 0 0 0 -1 -1 #> [32,] 0 0 0 -1 -1 #> [33,] 0 0 0 -1 -1 #> [34,] 0 0 0 -1 -1 #> [35,] 0 0 0 -1 -1 #> [36,] 0 0 0 -1 -1 #> [37,] 0 0 0 -1 -1 #> [38,] 0 0 0 -1 -1 #> [39,] 0 0 0 -1 -1 #> [40,] 0 0 0 -1 -1 #> [41,] 0 0 0 -1 -1 #> [42,] 0 0 0 -1 -1 #> [43,] 0 0 0 -1 -1 #> [44,] 0 0 0 -1 -1 #> [45,] 0 0 0 -1 -1 #> [46,] 0 0 0 -1 -1 #> [47,] 0 0 0 -1 -1 #> [48,] 0 0 0 -1 -1 #> [49,] 0 0 0 -1 -1 #> [50,] 0 0 0 -1 -1 #> [51,] 0 0 0 -1 -1 #> [52,] 0 0 0 -1 -1 #> [53,] 0 0 0 -1 -1 #> [54,] 0 0 0 -1 -1 #> [55,] 0 0 0 -1 -1 #> [56,] 0 0 0 -1 -1 #> [57,] 0 0 0 -1 -1 #> [58,] 0 0 0 -1 -1 #> [59,] 0 0 0 -1 -1 #> [60,] 0 0 0 -1 -1 #> [61,] 0 0 0 -1 -1 #> [62,] 0 0 0 -1 -1 #> [63,] 0 0 0 -1 -1 #> [64,] 0 0 0 -1 -1 #> [65,] 0 0 0 -1 -1 #> [66,] 0 0 0 -1 -1 #> [67,] 0 0 0 -1 -1 #> [68,] 0 0 0 -1 -1 #> [69,] 0 0 0 -1 -1 #> [70,] 0 0 0 -1 -1 #> [71,] 0 0 0 -1 -1 #> [72,] 0 0 0 -1 -1 #> [73,] 0 0 0 -1 -1 #> [74,] 0 0 0 -1 -1 #> [75,] 0 0 0 -1 -1 #> [76,] 0 0 0 -1 -1 #> [77,] 0 0 0 -1 -1 #> [78,] 0 0 0 -1 -1 #> [79,] 0 0 0 -1 -1 #> [80,] 0 0 0 -1 -1 #> [81,] 0 0 0 -1 -1 #> [82,] 0 0 0 -1 -1 #> [83,] 0 0 0 -1 -1 #> [84,] 0 0 0 -1 -1 #> [85,] 0 0 0 -1 -1 #> [86,] 0 0 0 -1 -1 #> [87,] 0 0 0 -1 -1 #> [88,] 0 0 0 -1 -1 #> [89,] 0 0 0 -1 -1 #> [90,] 0 0 0 -1 -1 #> [91,] 0 0 0 -1 -1 #> [92,] 0 0 0 -1 -1 #> [93,] 0 0 0 -1 -1 #> [94,] 0 0 0 -1 -1 #> [95,] 0 0 0 -1 -1 #> [96,] 0 0 0 -1 -1 #> [97,] 0 0 0 -1 -1 #> [98,] 0 0 0 -1 -1 #> [99,] 0 0 0 -1 -1 #> [100,] 0 0 0 -1 -1 #> [101,] 0 0 0 -1 -1 #> [102,] 0 0 0 -1 -1 #> [103,] 0 0 0 -1 -1 #> [104,] 0 0 0 -1 -1 #> [105,] 0 0 0 -1 -1 #> [106,] 0 0 0 -1 -1 #> [107,] 0 0 0 -1 -1 #> [108,] 0 0 0 -1 -1 #> [109,] 0 0 0 -1 -1 #> [110,] 0 0 0 -1 -1 #> [111,] 0 0 0 -1 -1 #> [112,] 0 0 0 -1 -1 #> [113,] 0 0 0 -1 -1 #> [114,] 0 0 0 -1 -1 #> [115,] 0 0 0 -1 -1 #> [116,] 0 0 0 -1 -1 #> [117,] 0 0 0 -1 -1 #> [118,] 0 0 0 -1 -1 #> [119,] 0 0 0 -1 -1 #> [120,] 0 0 0 -1 -1 #> [121,] 0 0 0 -1 -1 #> [122,] 0 0 0 -1 -1 #> [123,] 0 0 0 -1 -1 #> [124,] 0 0 0 -1 -1 #> [125,] 0 0 0 -1 -1 #> [126,] 0 0 0 -1 -1 #> [127,] 0 0 0 -1 -1 #> [128,] 0 0 0 -1 -1 #> [129,] 0 0 0 -1 -1 #> [130,] 0 0 0 -1 -1 #> [131,] 0 0 0 -1 -1 #> [132,] 0 0 0 -1 -1 #> [133,] 0 0 0 -1 -1 #> [134,] 0 0 0 -1 -1 #> [135,] 0 0 0 -1 -1 #> [136,] 0 0 0 -1 -1 #> [137,] 0 0 0 -1 -1 #> [138,] 0 0 0 -1 -1 #> [139,] 0 0 0 -1 -1 #> [140,] 0 0 0 -1 -1 #> [141,] 0 0 0 -1 -1 #> [142,] 0 0 0 -1 -1 #> [143,] 0 0 0 -1 -1 #> [144,] 0 0 0 -1 -1 #> [145,] 0 0 0 -1 -1 #> [146,] 0 0 0 -1 -1 #> [147,] 0 0 0 -1 -1 #> [148,] 0 0 0 -1 -1 #> [149,] 0 0 0 -1 -1 #> [150,] 0 0 0 -1 -1 #> [151,] 0 0 0 -1 -1 #> [152,] 0 0 0 -1 -1 #> [153,] 0 0 0 -1 -1 #> [154,] 0 0 0 -1 -1 #> [155,] 0 0 0 -1 -1 #> [156,] 0 0 0 -1 -1 #> [157,] 0 0 0 -1 -1 #> [158,] 0 0 0 -1 -1 #> [159,] 0 0 0 -1 -1 #> [160,] 0 0 0 -1 -1 #> [161,] 0 0 0 -1 -1 #> [162,] 0 0 0 -1 -1 #> [163,] 0 0 0 -1 -1 #> [164,] 0 0 0 -1 -1 #> [165,] 0 0 0 -1 -1 #> [166,] 0 0 0 -1 -1 #> [167,] 0 0 0 -1 -1 #> [168,] 0 0 0 -1 -1 #> [169,] 0 0 0 -1 -1 #> [170,] 0 0 0 -1 -1 #> [171,] 0 0 0 -1 -1 #> [172,] 0 0 0 -1 -1 #> [173,] 0 0 0 -1 -1 #> [174,] 0 0 0 -1 -1 #> [175,] 0 0 0 -1 -1 #> [176,] 0 0 0 -1 -1 #> [177,] 0 0 0 -1 -1 #> [178,] 0 0 0 -1 -1 #> [179,] 0 0 0 -1 -1 #> [180,] 0 0 0 -1 -1 #> [181,] 0 0 0 -1 -1 #> [182,] 0 0 0 -1 -1 #> [183,] 0 0 0 -1 -1 #> [184,] 0 0 0 -1 -1 #> [185,] 0 0 0 -1 -1 #> [186,] 0 0 0 -1 -1 #> [187,] 0 0 0 -1 -1 #> [188,] 0 0 0 -1 -1 #> [189,] 0 0 0 -1 -1 #> [190,] 0 0 0 -1 -1 #> [191,] 0 0 0 -1 -1 #> [192,] 0 0 0 -1 -1 #> [193,] 0 0 0 -1 -1 #> [194,] 0 0 0 -1 -1 #> [195,] 0 0 0 -1 -1 #> [196,] 0 0 0 -1 -1 #> [197,] 0 0 0 -1 -1 #> [198,] 0 0 0 -1 -1 #> [199,] 0 0 0 -1 -1 #> [200,] 0 0 0 -1 -1 #> [201,] 0 0 0 -1 -1 #> [202,] 0 0 0 -1 -1 #> [203,] 0 0 0 -1 -1 #> [204,] 0 0 0 -1 -1 #> [205,] 0 0 0 -1 -1 #> [206,] 0 0 0 -1 -1 #> [207,] 0 0 0 -1 -1 #> [208,] 0 0 0 -1 -1 #> [209,] 0 0 0 -1 -1 #> [210,] 0 0 0 -1 -1 #> [211,] 0 0 0 -1 -1 #> [212,] 0 0 0 -1 -1 #> [213,] 0 0 0 -1 -1 #> [214,] 0 0 0 -1 -1 #> [215,] 0 0 0 -1 -1 #> [216,] 0 0 0 -1 -1 #> [217,] 0 0 0 -1 -1 #> [218,] 0 0 0 -1 -1 #> [219,] 0 1 0 -1 -1 #> [220,] 1 0 0 -1 -1 #> [221,] 0 0 0 -1 -1 #> [222,] 0 0 0 -1 -1 #> [223,] 0 0 0 -1 -1 #> [224,] 0 0 0 -1 -1 #> [225,] 0 0 0 -1 -1 #> [226,] 0 0 0 -1 -1 #> [227,] 0 0 0 -1 -1 #> [228,] 0 0 0 -1 -1 #> [229,] 0 0 0 -1 -1 #> [230,] 0 0 0 -1 -1 #> [231,] 0 0 0 -1 -1 #> [232,] 0 0 0 -1 -1 #> [233,] 0 0 0 -1 -1 #> [234,] 0 0 0 -1 -1 #> [235,] 0 0 0 -1 -1 #> [236,] 0 0 0 -1 -1 #> [237,] 0 0 0 -1 -1 #> [238,] 0 0 0 -1 -1 #> [239,] 0 0 0 -1 -1 #> [240,] 0 0 0 -1 -1 #> [241,] 0 0 0 -1 -1 #> [242,] 0 0 0 -1 -1 #> [243,] 0 0 0 -1 -1 #> [244,] 0 0 0 -1 -1 #> [245,] 0 0 0 -1 -1 #> [246,] 0 0 0 -1 -1 #> [247,] 0 0 0 -1 -1 #> [248,] 0 0 0 -1 -1 #> [249,] 0 0 0 -1 -1 #> [250,] 0 0 0 -1 -1 #> [251,] 0 0 0 -1 -1 #> [252,] 0 0 0 -1 -1 #> [253,] 0 0 0 -1 -1 #> [254,] 0 0 0 -1 -1 #> [255,] 0 0 0 -1 -1 #> [256,] 0 0 0 -1 -1 #> [257,] 0 0 0 -1 -1 #> [258,] 0 0 0 -1 -1 #> [259,] 0 0 0 -1 -1 #> [260,] 0 0 0 -1 -1 #> [261,] 0 0 0 -1 -1 #> [262,] 0 0 0 -1 -1 #> [263,] 0 0 0 -1 -1 #> [264,] 0 0 0 -1 -1 #> [265,] 0 0 0 -1 -1 #> [266,] 0 0 0 -1 -1 #> [267,] 0 0 0 -1 -1 #> [268,] 0 0 0 -1 -1 #> [269,] 0 0 0 -1 -1 #> [270,] 0 0 0 -1 -1 #> [271,] 0 0 0 -1 -1 #> [272,] 0 0 0 -1 -1 #> [273,] 0 0 0 -1 -1 #> [274,] 0 0 0 -1 -1 #> [275,] 0 0 0 -1 -1 #> [276,] 0 0 0 -1 -1 #> [277,] 0 0 1 -1 -1 #> [278,] 0 0 0 -1 -1 #> [279,] 0 0 0 -1 -1 #> [280,] 0 0 0 -1 0 #> [281,] 0 0 0 -1 0 #> [282,] 0 0 0 -1 0 #> [283,] 0 0 0 -1 0 #> [284,] 0 0 0 -1 0 #> [285,] 0 0 0 -1 0 #> [286,] 0 0 0 -1 0 #> [287,] 0 0 0 -1 0 #> [288,] 0 0 0 -1 0 #> [289,] 0 0 0 -1 0 #> [290,] 0 0 0 -1 0 #> [291,] 0 0 0 -1 0 #> [292,] 0 0 0 -1 0 #> [293,] 0 0 0 -1 0 #> [294,] 0 0 0 -1 0 #> [295,] 0 0 0 -1 0 #> [296,] 0 0 0 -1 0 #> [297,] 0 0 0 -1 0 #> [298,] 0 0 0 -1 0 #> [299,] 0 0 0 -1 0 #> [300,] 0 0 0 -1 0 #> [301,] 0 0 0 -1 0 #> [302,] 0 0 0 -1 0 #> [303,] 0 0 0 -1 0 #> [304,] 0 0 0 -1 0 #> [305,] 0 0 0 -1 0 #> [306,] 0 0 0 -1 0 #> [307,] 0 0 0 -1 0 #> [308,] 0 0 0 -1 0 #> [309,] 0 0 0 -1 0 #> [310,] 0 0 0 -1 0 #> [311,] 0 0 0 -1 0 #> [312,] 0 0 0 -1 0 #> [313,] 0 0 0 -1 0 #> [314,] 0 0 0 -1 0 #> [315,] 0 0 0 -1 0 #> [316,] 0 0 0 -1 0 #> [317,] 0 0 0 -1 0 #> [318,] 0 0 0 -1 0 #> [319,] 0 0 0 -1 0 #> [320,] 0 0 0 -1 0 #> [321,] 0 0 0 -1 0 #> [322,] 0 0 0 -1 0 #> [323,] 0 0 0 -1 0 #> [324,] 0 0 0 -1 0 #> [325,] 0 0 0 -1 0 #> [326,] 0 0 0 -1 0 #> [327,] 0 0 0 -1 0 #> [328,] 0 0 0 -1 0 #> [329,] 0 0 0 -1 0 #> [330,] 0 0 0 -1 0 #> [331,] 0 0 0 -1 0 #> [332,] 0 0 0 -1 0 #> [333,] 0 0 0 -1 0 #> [334,] 0 0 0 -1 0 #> [335,] 0 0 0 -1 0 #> [336,] 0 0 0 -1 0 #> [337,] 0 0 0 -1 0 #> [338,] 0 0 0 -1 0 #> [339,] 0 0 0 -1 0 #> [340,] 0 0 0 -1 0 #> [341,] 0 0 0 -1 0 #> [342,] 0 0 0 -1 0 #> [343,] 0 0 0 -1 0 #> [344,] 0 0 0 -1 0 #> [345,] 0 0 0 -1 0 #> [346,] 0 0 0 -1 0 #> [347,] 0 0 0 -1 0 #> [348,] 0 0 0 -1 0 #> [349,] 0 0 0 -1 0 #> [350,] 0 0 0 -1 0 #> [351,] 0 0 0 -1 0 #> [352,] 0 0 0 -1 0 #> [353,] 0 0 0 -1 0 #> [354,] 0 0 0 -1 0 #> [355,] 0 0 0 -1 0 #> [356,] 0 0 0 -1 0 #> [357,] 0 0 0 -1 0 #> [358,] 0 0 0 -1 0 #> [359,] 0 0 0 -1 0 #> [360,] 0 0 0 -1 0 #> [361,] 0 0 0 -1 0 #> [362,] 0 0 0 -1 0 #> [363,] 0 0 0 -1 0 #> [364,] 0 0 0 -1 0 #> [365,] 0 0 0 -1 0 #> [366,] 0 0 0 -1 0 #> [367,] 0 0 0 -1 0 #> [368,] 0 0 0 -1 0 #> [369,] 0 0 0 -1 0 #> [370,] 0 0 0 -1 0 #> [371,] 0 0 0 -1 0 #> [372,] 0 0 0 -1 0 #> [373,] 0 0 0 -1 0 #> [374,] 0 0 0 -1 0 #> [375,] 0 0 0 -1 0 #> [376,] 0 0 0 -1 0 #> [377,] 0 0 0 -1 0 #> [378,] 0 0 0 -1 0 #> [379,] 0 0 0 -1 0 #> [380,] 0 0 0 -1 0 #> [381,] 0 0 0 -1 0 #> [382,] 0 0 0 -1 0 #> [383,] 0 0 0 -1 0 #> [384,] 0 0 0 -1 0 #> [385,] 0 0 0 -1 0 #> [386,] 0 0 0 -1 0 #> [387,] 0 0 0 -1 0 #> [388,] 0 0 0 -1 0 #> [389,] 0 0 0 -1 0 #> [390,] 0 0 0 -1 0 #> [391,] 0 0 0 -1 0 #> [392,] 0 0 0 -1 0 #> [393,] 0 0 0 -1 0 #> [394,] 0 0 0 -1 0 #> [395,] 0 0 0 -1 0 #> [396,] 0 0 0 -1 0 #> [397,] 0 0 0 -1 0 #> [398,] 0 0 0 -1 0 #> [399,] 0 0 0 -1 0 #> [400,] 0 0 0 0 0 #> [401,] 0 0 0 0 0 #> [402,] 0 0 0 0 0 #> [403,] 0 0 0 0 0 #> [404,] 0 0 0 0 0 #> [405,] 0 0 0 0 0 #> [406,] 0 0 0 0 0 #> [407,] 0 0 0 0 0 #> [408,] 0 0 0 0 0 #> [409,] 0 0 0 0 0 #> [410,] 0 0 0 0 0 #> [411,] 0 0 0 0 0 #> [412,] 0 0 0 0 0 #> [413,] 0 0 0 0 0 #> [414,] 0 0 0 0 0 #> [415,] 0 0 0 0 0 #> [416,] 0 0 0 0 0 #> [417,] 0 0 0 0 0 #> [418,] 0 0 0 0 0 #> [419,] 0 0 0 0 0 #> [420,] 0 0 0 0 0 #> [421,] 0 0 0 0 0 #> [422,] 0 0 0 0 0 #> [423,] 0 0 0 0 0 #> [424,] 0 0 0 0 0 #> [425,] 0 0 0 0 0 #> #> $model$b #> [1] -210.91745 199.66569 -192.39785 84.32270 -77.78577 #> #> $model$bcov #> [,1] [,2] [,3] [,4] [,5] #> [1,] 1.258544e+03 5.076022e+01 -8.906666e-01 -1.197400e-05 -0.827996523 #> [2,] 5.076022e+01 1.258545e+03 -9.422003e-01 -1.454505e-05 -1.048251277 #> [3,] -8.906666e-01 -9.422003e-01 1.322416e+03 5.431398e-04 155.010519468 #> [4,] -1.197400e-05 -1.454505e-05 5.431398e-04 3.654102e+02 0.004446791 #> [5,] -8.279965e-01 -1.048251e+00 1.550105e+02 4.446791e-03 364.501084888 #> #> $model$linearized #> [1] 466.6369 509.1369 450.3369 465.6369 444.9369 471.6369 459.2369 #> [8] 529.4369 895.8369 385.7369 384.5369 478.6369 509.9369 517.1369 #> [15] 468.9369 474.8369 464.7369 489.2369 491.8369 575.2369 970.2369 #> [22] 421.2369 421.0369 491.2369 493.8369 604.4369 506.9369 549.9369 #> [29] 509.9369 529.3369 563.1369 629.7369 1045.9369 522.8369 459.0369 #> [36] 532.3369 594.2369 706.8369 568.3369 609.3369 589.0369 569.6369 #> [43] 643.6369 703.6369 1194.0369 577.0369 484.7369 553.9369 600.8369 #> [50] 758.1369 559.9369 669.7369 587.6369 668.4369 672.1369 707.4369 #> [57] 1374.4369 620.4369 519.7369 606.4369 680.6369 720.5369 677.0369 #> [64] 727.4369 608.1369 678.8369 715.6369 749.7369 1466.6369 637.5369 #> [71] 557.6369 684.6369 722.2369 747.3369 728.5369 690.0369 657.4369 #> [78] 729.8369 736.1369 876.8369 1576.5369 638.0369 558.5369 725.5369 #> [85] 704.1369 771.3369 792.8369 721.6369 730.3369 764.4369 758.2369 #> [92] 930.3369 1575.9369 684.7369 592.7369 733.3369 750.6369 822.0369 #> [99] 838.9369 716.8369 765.9369 747.6369 793.1369 938.0369 1569.6369 #> [106] 700.5369 611.2369 725.7369 754.7369 834.7369 753.4369 801.0369 #> [113] 776.9369 748.0369 865.1369 951.2369 1606.8369 746.5369 672.4369 #> [120] 708.0369 837.9369 885.1369 832.5369 794.7369 730.1369 826.3369 #> [127] 909.0369 975.1369 1657.4369 768.6369 649.5369 760.6369 847.2369 #> [134] 913.1369 893.6369 778.0369 735.2369 851.2369 893.4369 976.5369 #> [141] 1717.0369 752.2369 670.9369 828.0369 838.2369 914.5369 919.1369 #> [148] 789.4369 805.3369 893.5369 941.1369 1006.9369 1824.0369 758.9369 #> [155] 689.0369 817.7369 912.5369 933.7369 913.3369 887.1369 880.4369 #> [162] 863.3369 927.1369 1073.9369 1863.7369 839.6369 743.6369 818.5369 #> [169] 901.7369 969.3369 915.1369 914.5369 895.4369 840.2369 940.2369 #> [176] 1088.1369 1844.1369 847.4369 733.9369 864.4369 855.5369 1001.3369 #> [183] 836.7369 977.6369 842.5369 945.6369 983.4369 1117.8369 1885.6369 #> [190] 923.8369 722.7369 829.4369 976.6369 976.7369 855.9369 1048.8369 #> [197] 876.4369 945.9369 1027.8369 1144.2369 1982.2369 948.5369 744.9369 #> [204] 909.7369 959.7369 1017.7369 900.9369 1061.0369 906.0369 1008.8369 #> [211] 1050.2369 1213.7369 2076.1369 931.4369 804.7369 908.4369 1031.2369 #> [218] 1058.8369 972.3712 1076.7544 1015.7369 1061.1369 1076.9369 1239.0369 #> [225] 2184.0369 978.4369 821.1369 1024.0369 1045.7369 1130.0369 1031.4369 #> [232] 1107.3369 969.5369 1019.4369 1138.5369 1351.3369 2276.0369 1034.4369 #> [239] 847.9369 1050.4369 1081.8369 1197.4369 1149.5369 1082.2369 1072.4369 #> [246] 1066.6369 1217.9369 1501.6369 2345.1369 1105.8369 907.0369 1099.2369 #> [253] 1228.9369 1243.6369 1244.4369 1188.5369 1107.7369 1204.7369 1322.6369 #> [260] 1534.7369 2430.7369 1189.4369 996.3369 1137.9369 1283.6369 1286.8369 #> [267] 1390.6369 1312.4369 1173.3369 1324.4369 1364.8369 1543.2369 2507.3369 #> [274] 1252.8369 1043.8369 1307.3369 1352.6348 1270.7369 1460.7369 1374.4227 #> [281] 1295.0227 1362.1227 1398.7227 1624.7227 2620.3227 1278.0227 1122.0227 #> [288] 1288.8227 1432.9227 1351.9227 1513.3227 1496.3227 1323.5227 1303.4227 #> [295] 1428.9227 1707.6227 2695.4227 1351.6227 1131.3227 1415.9227 1386.9227 #> [302] 1449.4227 1575.8227 1546.6227 1399.8227 1437.6227 1524.9227 1772.2227 #> [309] 2831.3227 1482.1227 1224.8227 1436.0227 1480.9227 1505.4227 1485.9227 #> [316] 1666.6227 1352.7227 1467.6227 1536.7227 1760.2227 2970.4227 1535.3227 #> [323] 1140.9227 1471.2227 1593.4227 1603.7227 1584.8227 1655.0227 1425.8227 #> [330] 1484.2227 1618.6227 1820.9227 2879.4227 1553.4227 1196.2227 1464.2227 #> [337] 1474.0227 1511.5227 1635.7227 1665.3227 1408.3227 1506.3227 1549.2227 #> [344] 1789.8227 2836.7227 1496.9227 1201.8227 1405.9227 1556.9227 1493.2227 #> [351] 1556.2227 1616.8227 1377.8227 1430.0227 1489.0227 1744.3227 2814.8227 #> [358] 1446.7227 1216.0227 1433.5227 1475.5227 1541.2227 1700.7227 1507.7227 #> [365] 1443.3227 1452.1227 1526.9227 1757.2227 2837.6227 1481.7227 1197.9227 #> [372] 1481.6227 1423.4227 1526.2227 1621.7227 1474.9227 1421.5227 1443.7227 #> [379] 1547.6227 1753.2227 2809.8227 1535.3227 1149.2227 1377.5227 1527.2227 #> [386] 1496.1227 1545.9227 1585.9227 1338.5227 1440.7227 1563.0227 1772.0227 #> [393] 2841.2227 1555.5227 1138.1227 1451.5227 1526.5227 1513.0227 1565.2227 #> [400] 1540.9000 1331.9000 1400.1000 1566.3000 1730.5000 2913.6000 1519.2000 #> [407] 1155.8000 1451.5000 1451.0000 1449.7000 1596.1000 1468.3000 1293.9000 #> [414] 1393.5000 1497.4000 1684.3000 2850.4000 1428.5000 1092.4000 1370.3000 #> [421] 1522.6000 1452.4000 1557.2000 1445.5000 1303.1000 #> #> #> $likelihood #> $likelihood$initial #> $likelihood$initial$ll #> [1] -2218.964 #> #> $likelihood$initial$ssq #> [1] 1139531 #> #> $likelihood$initial$nobs #> [1] 425 #> #> $likelihood$initial$neffective #> [1] -1 #> #> $likelihood$initial$nparams #> [1] 3 #> #> $likelihood$initial$df #> [1] 409 #> #> $likelihood$initial$aic #> [1] 4443.928 #> #> $likelihood$initial$aicc #> [1] 4443.987 #> #> $likelihood$initial$bic #> [1] 4455.991 #> #> $likelihood$initial$bic2 #> [1] 10.81551 #> #> $likelihood$initial$bicc #> [1] 7.954332 #> #> $likelihood$initial$hannanquinn #> [1] 4448.7 #> #> #> $likelihood$final #> $likelihood$final$ll #> [1] -2167.948 #> #> $likelihood$final$ssq #> [1] 892303.8 #> #> $likelihood$final$nobs #> [1] 425 #> #> $likelihood$final$neffective #> [1] -1 #> #> $likelihood$final$nparams #> [1] 6 #> #> $likelihood$final$df #> [1] 406 #> #> $likelihood$final$aic #> [1] 4347.896 #> #> $likelihood$final$aicc #> [1] 4348.104 #> #> $likelihood$final$bic #> [1] 4372.023 #> #> $likelihood$final$bic2 #> [1] 10.61171 #> #> $likelihood$final$bicc #> [1] 7.753609 #> #> $likelihood$final$hannanquinn #> [1] 4357.439 #> #> #> #> attr(,\"class\") #> [1] \"JD3_REGARIMA_OUTLIERS\""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/userdefined_variables_x13.html","id":null,"dir":"Reference","previous_headings":"","what":"Display a list of all the available output objects — userdefined_variables_x13","title":"Display a list of all the available output objects — userdefined_variables_x13","text":"Function generating comprehensive list available output variables (series, parameters, diagnostics) estimation process x13(), regarima() x11() functions. items available default estimation output remainder can added using userdefined parameter. User-defined objects can retrieved list lists generated estimation process","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/userdefined_variables_x13.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display a list of all the available output objects — userdefined_variables_x13","text":"","code":"userdefined_variables_x13(x = c(\"X-13\", \"RegArima\", \"X-11\"))"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/userdefined_variables_x13.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display a list of all the available output objects — userdefined_variables_x13","text":"x character indicate estimation function output items list displayed.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/userdefined_variables_x13.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display a list of all the available output objects — userdefined_variables_x13","text":"vector containing names available output objects (series, diagnostics, parameters)","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/userdefined_variables_x13.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Display a list of all the available output objects — userdefined_variables_x13","text":"information examples related 'JDemetra+' features online documentation: https://jdemetra-new-documentation.netlify.app/","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/userdefined_variables_x13.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display a list of all the available output objects — userdefined_variables_x13","text":"","code":"userdefined_variables_x13(\"x13\") #> [1] \"adjust\" #> [2] \"arima.bd\" #> [3] \"arima.bp\" #> [4] \"arima.bphi(*)\" #> [5] \"arima.bq\" #> [6] \"arima.btheta(*)\" #> [7] \"arima.d\" #> [8] \"arima.p\" #> [9] \"arima.phi(*)\" #> [10] \"arima.q\" #> [11] \"arima.theta(*)\" #> [12] \"benchmarking.original\" #> [13] \"benchmarking.result\" #> [14] \"benchmarking.target\" #> [15] \"cal\" #> [16] \"cal_b\" #> [17] \"cal_b(?)\" #> [18] \"cal_f\" #> [19] \"cal_f(?)\" #> [20] \"decomposition.b1\" #> [21] \"decomposition.b10\" #> [22] \"decomposition.b11\" #> [23] \"decomposition.b13\" #> [24] \"decomposition.b17\" #> [25] \"decomposition.b2\" #> [26] \"decomposition.b20\" #> [27] \"decomposition.b3\" #> [28] \"decomposition.b4\" #> [29] \"decomposition.b5\" #> [30] \"decomposition.b6\" #> [31] \"decomposition.b7\" #> [32] \"decomposition.b8\" #> [33] \"decomposition.b9\" #> [34] \"decomposition.c1\" #> [35] \"decomposition.c10\" #> [36] \"decomposition.c11\" #> [37] \"decomposition.c13\" #> [38] \"decomposition.c17\" #> [39] \"decomposition.c2\" #> [40] \"decomposition.c20\" #> [41] \"decomposition.c4\" #> [42] \"decomposition.c5\" #> [43] \"decomposition.c6\" #> [44] \"decomposition.c7\" #> [45] \"decomposition.c9\" #> [46] \"decomposition.d1\" #> [47] \"decomposition.d10\" #> [48] \"decomposition.d11\" #> [49] \"decomposition.d12\" #> [50] \"decomposition.d13\" #> [51] \"decomposition.d2\" #> [52] \"decomposition.d4\" #> [53] \"decomposition.d5\" #> [54] \"decomposition.d6\" #> [55] \"decomposition.d7\" #> [56] \"decomposition.d8\" #> [57] \"decomposition.d9\" #> [58] \"decomposition.d9-global-msr\" #> [59] \"decomposition.d9-msr\" #> [60] \"decomposition.d9-msr-table\" #> [61] \"decomposition.i_cmp\" #> [62] \"decomposition.s_cmp\" #> [63] \"decomposition.s_cmp_b\" #> [64] \"decomposition.s_cmp_f\" #> [65] \"decomposition.sa_cmp\" #> [66] \"decomposition.sa_cmp_b\" #> [67] \"decomposition.sa_cmp_f\" #> [68] \"decomposition.seasonal-filters\" #> [69] \"decomposition.si_cmp\" #> [70] \"decomposition.t_cmp\" #> [71] \"decomposition.t_cmp_b\" #> [72] \"decomposition.t_cmp_f\" #> [73] \"decomposition.trend-filter\" #> [74] \"decomposition.x11-all\" #> [75] \"decomposition.y_cmp\" #> [76] \"decomposition.y_cmp_b\" #> [77] \"decomposition.y_cmp_f\" #> [78] \"det\" #> [79] \"det_b(?)\" #> [80] \"det_f(?)\" #> [81] \"det_i\" #> [82] \"det_i_b(?)\" #> [83] \"det_i_f(?)\" #> [84] \"det_s\" #> [85] \"det_s_b(?)\" #> [86] \"det_s_f(?)\" #> [87] \"det_t\" #> [88] \"det_t_b(?)\" #> [89] \"det_t_f(?)\" #> [90] \"diagnostics.fcast-insample-mean\" #> [91] \"diagnostics.fcast-outsample-mean\" #> [92] \"diagnostics.fcast-outsample-variance\" #> [93] \"diagnostics.seas-i-combined\" #> [94] \"diagnostics.seas-i-combined3\" #> [95] \"diagnostics.seas-i-evolutive\" #> [96] \"diagnostics.seas-i-f\" #> [97] \"diagnostics.seas-i-friedman\" #> [98] \"diagnostics.seas-i-kw\" #> [99] \"diagnostics.seas-i-periodogram\" #> [100] \"diagnostics.seas-i-qs\" #> [101] \"diagnostics.seas-i-spectralpeaks\" #> [102] \"diagnostics.seas-i-stable\" #> [103] \"diagnostics.seas-lin-combined\" #> [104] \"diagnostics.seas-lin-evolutive\" #> [105] \"diagnostics.seas-lin-f\" #> [106] \"diagnostics.seas-lin-friedman\" #> [107] \"diagnostics.seas-lin-kw\" #> [108] \"diagnostics.seas-lin-periodogram\" #> [109] \"diagnostics.seas-lin-qs\" #> [110] \"diagnostics.seas-lin-spectralpeaks\" #> [111] \"diagnostics.seas-lin-stable\" #> [112] \"diagnostics.seas-res-combined\" #> [113] \"diagnostics.seas-res-combined3\" #> [114] \"diagnostics.seas-res-evolutive\" #> [115] \"diagnostics.seas-res-f\" #> [116] \"diagnostics.seas-res-friedman\" #> [117] \"diagnostics.seas-res-kw\" #> [118] \"diagnostics.seas-res-periodogram\" #> [119] \"diagnostics.seas-res-qs\" #> [120] \"diagnostics.seas-res-spectralpeaks\" #> [121] \"diagnostics.seas-res-stable\" #> [122] \"diagnostics.seas-sa-ac1\" #> [123] \"diagnostics.seas-sa-combined\" #> [124] \"diagnostics.seas-sa-combined3\" #> [125] \"diagnostics.seas-sa-evolutive\" #> [126] \"diagnostics.seas-sa-f\" #> [127] \"diagnostics.seas-sa-friedman\" #> [128] \"diagnostics.seas-sa-kw\" #> [129] \"diagnostics.seas-sa-periodogram\" #> [130] \"diagnostics.seas-sa-qs\" #> [131] \"diagnostics.seas-sa-spectralpeaks\" #> [132] \"diagnostics.seas-sa-stable\" #> [133] \"diagnostics.seas-si-combined\" #> [134] \"diagnostics.seas-si-combined3\" #> [135] \"diagnostics.seas-si-evolutive\" #> [136] \"diagnostics.seas-si-stable\" #> [137] \"diagnostics.td-i-all\" #> [138] \"diagnostics.td-i-last\" #> [139] \"diagnostics.td-res-all\" #> [140] \"diagnostics.td-res-last\" #> [141] \"diagnostics.td-sa-all\" #> [142] \"diagnostics.td-sa-last\" #> [143] \"ee\" #> [144] \"ee_b(?)\" #> [145] \"ee_f(?)\" #> [146] \"finals.d11\" #> [147] \"finals.d11a\" #> [148] \"finals.d11b\" #> [149] \"finals.d12\" #> [150] \"finals.d12a\" #> [151] \"finals.d12b\" #> [152] \"finals.d13\" #> [153] \"finals.d16\" #> [154] \"finals.d16a\" #> [155] \"finals.d16b\" #> [156] \"finals.d18\" #> [157] \"finals.d18a\" #> [158] \"finals.d18b\" #> [159] \"finals.e1\" #> [160] \"finals.e11\" #> [161] \"finals.e2\" #> [162] \"finals.e3\" #> [163] \"i\" #> [164] \"l\" #> [165] \"likelihood.adjustedll\" #> [166] \"likelihood.aic\" #> [167] \"likelihood.aicc\" #> [168] \"likelihood.bic\" #> [169] \"likelihood.bic2\" #> [170] \"likelihood.bicc\" #> [171] \"likelihood.df\" #> [172] \"likelihood.hannanquinn\" #> [173] \"likelihood.ll\" #> [174] \"likelihood.neffectiveobs\" #> [175] \"likelihood.nobs\" #> [176] \"likelihood.nparams\" #> [177] \"likelihood.ssqerr\" #> [178] \"log\" #> [179] \"m-statistics.m1\" #> [180] \"m-statistics.m10\" #> [181] \"m-statistics.m11\" #> [182] \"m-statistics.m2\" #> [183] \"m-statistics.m3\" #> [184] \"m-statistics.m4\" #> [185] \"m-statistics.m5\" #> [186] \"m-statistics.m6\" #> [187] \"m-statistics.m7\" #> [188] \"m-statistics.m8\" #> [189] \"m-statistics.m9\" #> [190] \"m-statistics.q\" #> [191] \"m-statistics.q-m2\" #> [192] \"mhe\" #> [193] \"mhe_b(?)\" #> [194] \"mhe_f(?)\" #> [195] \"omhe\" #> [196] \"omhe_b(?)\" #> [197] \"omhe_f(?)\" #> [198] \"out\" #> [199] \"out_b(?)\" #> [200] \"out_f(?)\" #> [201] \"out_i\" #> [202] \"out_i_b(?)\" #> [203] \"out_i_f(?)\" #> [204] \"out_s\" #> [205] \"out_s_b(?)\" #> [206] \"out_s_f(?)\" #> [207] \"out_t\" #> [208] \"out_t_b(?)\" #> [209] \"out_t_f(?)\" #> [210] \"period\" #> [211] \"preadjustment.a1\" #> [212] \"preadjustment.a1a\" #> [213] \"preadjustment.a1b\" #> [214] \"preadjustment.a6\" #> [215] \"preadjustment.a7\" #> [216] \"preadjustment.a8\" #> [217] \"preadjustment.a8i\" #> [218] \"preadjustment.a8s\" #> [219] \"preadjustment.a8t\" #> [220] \"preadjustment.a9\" #> [221] \"preadjustment.a9sa\" #> [222] \"preadjustment.a9ser\" #> [223] \"preadjustment.a9u\" #> [224] \"reg_i\" #> [225] \"reg_i_b(?)\" #> [226] \"reg_i_f(?)\" #> [227] \"reg_s\" #> [228] \"reg_s_b(?)\" #> [229] \"reg_s_f(?)\" #> [230] \"reg_sa\" #> [231] \"reg_sa_b(?)\" #> [232] \"reg_sa_f(?)\" #> [233] \"reg_t\" #> [234] \"reg_t_b(?)\" #> [235] \"reg_t_f(?)\" #> [236] \"reg_u\" #> [237] \"reg_u_b(?)\" #> [238] \"reg_u_f(?)\" #> [239] \"reg_y\" #> [240] \"reg_y_b(?)\" #> [241] \"reg_y_f(?)\" #> [242] \"regression.description\" #> [243] \"regression.details.coefficients\" #> [244] \"regression.details.covar\" #> [245] \"regression.details.covar-ml\" #> [246] \"regression.easter\" #> [247] \"regression.espan.end\" #> [248] \"regression.espan.missing\" #> [249] \"regression.espan.n\" #> [250] \"regression.espan.start\" #> [251] \"regression.leaster\" #> [252] \"regression.lp\" #> [253] \"regression.mean\" #> [254] \"regression.missing(*)\" #> [255] \"regression.ml.parameters\" #> [256] \"regression.ml.pcorr\" #> [257] \"regression.ml.pcovar\" #> [258] \"regression.ml.pcovar-ml\" #> [259] \"regression.ml.pscore\" #> [260] \"regression.mu\" #> [261] \"regression.nao\" #> [262] \"regression.nlp\" #> [263] \"regression.nls\" #> [264] \"regression.nmh\" #> [265] \"regression.nout\" #> [266] \"regression.nso\" #> [267] \"regression.ntc\" #> [268] \"regression.ntd\" #> [269] \"regression.nusers\" #> [270] \"regression.out(*)\" #> [271] \"regression.outlier(*)\" #> [272] \"regression.td(*)\" #> [273] \"regression.type\" #> [274] \"regression.user(*)\" #> [275] \"residuals.bp\" #> [276] \"residuals.bp2\" #> [277] \"residuals.doornikhansen\" #> [278] \"residuals.kurtosis\" #> [279] \"residuals.lb\" #> [280] \"residuals.lb2\" #> [281] \"residuals.lruns\" #> [282] \"residuals.ludruns\" #> [283] \"residuals.mean\" #> [284] \"residuals.nruns\" #> [285] \"residuals.nudruns\" #> [286] \"residuals.res\" #> [287] \"residuals.seasbp\" #> [288] \"residuals.seaslb\" #> [289] \"residuals.ser\" #> [290] \"residuals.skewness\" #> [291] \"residuals.tsres\" #> [292] \"residuals.type\" #> [293] \"s\" #> [294] \"s_b\" #> [295] \"s_f\" #> [296] \"sa\" #> [297] \"sa_b\" #> [298] \"sa_f\" #> [299] \"span.end\" #> [300] \"span.missing\" #> [301] \"span.n\" #> [302] \"span.start\" #> [303] \"t\" #> [304] \"t_b\" #> [305] \"t_f\" #> [306] \"tde\" #> [307] \"tde_b(?)\" #> [308] \"tde_f(?)\" #> [309] \"variancedecomposition.cycle\" #> [310] \"variancedecomposition.irregular\" #> [311] \"variancedecomposition.others\" #> [312] \"variancedecomposition.seasonality\" #> [313] \"variancedecomposition.tdh\" #> [314] \"variancedecomposition.total\" #> [315] \"y\" #> [316] \"y_b\" #> [317] \"y_b(?)\" #> [318] \"y_eb(?)\" #> [319] \"y_ef(?)\" #> [320] \"y_f\" #> [321] \"y_f(?)\" #> [322] \"yc\" #> [323] \"ycal\" #> [324] \"ycal_f(?)\" userdefined_variables_x13(\"regarima\") #> [1] \"adjust\" \"arima.bd\" #> [3] \"arima.bp\" \"arima.bphi(*)\" #> [5] \"arima.bq\" \"arima.btheta(*)\" #> [7] \"arima.d\" \"arima.p\" #> [9] \"arima.phi(*)\" \"arima.q\" #> [11] \"arima.theta(*)\" \"cal\" #> [13] \"cal_b(?)\" \"cal_f(?)\" #> [15] \"det\" \"det_b(?)\" #> [17] \"det_f(?)\" \"det_i\" #> [19] \"det_i_b(?)\" \"det_i_f(?)\" #> [21] \"det_s\" \"det_s_b(?)\" #> [23] \"det_s_f(?)\" \"det_t\" #> [25] \"det_t_b(?)\" \"det_t_f(?)\" #> [27] \"ee\" \"ee_b(?)\" #> [29] \"ee_f(?)\" \"l\" #> [31] \"likelihood.adjustedll\" \"likelihood.aic\" #> [33] \"likelihood.aicc\" \"likelihood.bic\" #> [35] \"likelihood.bic2\" \"likelihood.bicc\" #> [37] \"likelihood.df\" \"likelihood.hannanquinn\" #> [39] \"likelihood.ll\" \"likelihood.neffectiveobs\" #> [41] \"likelihood.nobs\" \"likelihood.nparams\" #> [43] \"likelihood.ssqerr\" \"log\" #> [45] \"mhe\" \"mhe_b(?)\" #> [47] \"mhe_f(?)\" \"omhe\" #> [49] \"omhe_b(?)\" \"omhe_f(?)\" #> [51] \"out\" \"out_b(?)\" #> [53] \"out_f(?)\" \"out_i\" #> [55] \"out_i_b(?)\" \"out_i_f(?)\" #> [57] \"out_s\" \"out_s_b(?)\" #> [59] \"out_s_f(?)\" \"out_t\" #> [61] \"out_t_b(?)\" \"out_t_f(?)\" #> [63] \"period\" \"reg_i\" #> [65] \"reg_i_b(?)\" \"reg_i_f(?)\" #> [67] \"reg_s\" \"reg_s_b(?)\" #> [69] \"reg_s_f(?)\" \"reg_sa\" #> [71] \"reg_sa_b(?)\" \"reg_sa_f(?)\" #> [73] \"reg_t\" \"reg_t_b(?)\" #> [75] \"reg_t_f(?)\" \"reg_u\" #> [77] \"reg_u_b(?)\" \"reg_u_f(?)\" #> [79] \"reg_y\" \"reg_y_b(?)\" #> [81] \"reg_y_f(?)\" \"regression.description\" #> [83] \"regression.details.coefficients\" \"regression.details.covar\" #> [85] \"regression.details.covar-ml\" \"regression.easter\" #> [87] \"regression.espan.end\" \"regression.espan.missing\" #> [89] \"regression.espan.n\" \"regression.espan.start\" #> [91] \"regression.leaster\" \"regression.lp\" #> [93] \"regression.mean\" \"regression.missing(*)\" #> [95] \"regression.ml.parameters\" \"regression.ml.pcorr\" #> [97] \"regression.ml.pcovar\" \"regression.ml.pcovar-ml\" #> [99] \"regression.ml.pscore\" \"regression.mu\" #> [101] \"regression.nao\" \"regression.nlp\" #> [103] \"regression.nls\" \"regression.nmh\" #> [105] \"regression.nout\" \"regression.nso\" #> [107] \"regression.ntc\" \"regression.ntd\" #> [109] \"regression.nusers\" \"regression.out(*)\" #> [111] \"regression.outlier(*)\" \"regression.td(*)\" #> [113] \"regression.type\" \"regression.user(*)\" #> [115] \"residuals.bp\" \"residuals.bp2\" #> [117] \"residuals.doornikhansen\" \"residuals.kurtosis\" #> [119] \"residuals.lb\" \"residuals.lb2\" #> [121] \"residuals.lruns\" \"residuals.ludruns\" #> [123] \"residuals.mean\" \"residuals.nruns\" #> [125] \"residuals.nudruns\" \"residuals.res\" #> [127] \"residuals.seasbp\" \"residuals.seaslb\" #> [129] \"residuals.ser\" \"residuals.skewness\" #> [131] \"residuals.tsres\" \"residuals.type\" #> [133] \"span.end\" \"span.missing\" #> [135] \"span.n\" \"span.start\" #> [137] \"tde\" \"tde_b(?)\" #> [139] \"tde_f(?)\" \"y\" #> [141] \"y_b(?)\" \"y_eb(?)\" #> [143] \"y_ef(?)\" \"y_f(?)\" #> [145] \"yc\" \"ycal\" #> [147] \"ycal_f(?)\" userdefined_variables_x13(\"x11\") #> [1] \"b1\" \"b10\" \"b11\" \"b13\" #> [5] \"b17\" \"b2\" \"b20\" \"b3\" #> [9] \"b4\" \"b5\" \"b6\" \"b7\" #> [13] \"b8\" \"b9\" \"c1\" \"c10\" #> [17] \"c11\" \"c13\" \"c17\" \"c2\" #> [21] \"c20\" \"c4\" \"c5\" \"c6\" #> [25] \"c7\" \"c9\" \"d1\" \"d10\" #> [29] \"d11\" \"d12\" \"d13\" \"d2\" #> [33] \"d4\" \"d5\" \"d6\" \"d7\" #> [37] \"d8\" \"d9\" \"d9-global-msr\" \"d9-msr\" #> [41] \"d9-msr-table\" \"seasonal-filters\" \"trend-filter\" \"x11-all\""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11.html","id":null,"dir":"Reference","previous_headings":"","what":"X-11 Decomposition Algorithm — x11","title":"X-11 Decomposition Algorithm — x11","text":"X-11 Decomposition Algorithm","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"X-11 Decomposition Algorithm — x11","text":"","code":"x11(ts, spec = x11_spec(), userdefined = NULL)"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"X-11 Decomposition Algorithm — x11","text":"ts univariate time series. spec specification. userdefined vector containing additional output variables (see x13_dictionary()).","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"X-11 Decomposition Algorithm — x11","text":"","code":"y <- rjd3toolkit::ABS$X0.2.09.10.M x11_spec <- x11_spec() x11(y, x11_spec) #> Last values #> d1 d2 d4 d5 d6 d7 d8 d9 #> Mar 2017 1370.300 NA NA 0.8961795 1529.046 1549.437 0.8843860 NaN #> Apr 2017 1508.745 NA NA 0.9370285 1610.138 1551.362 0.9814599 0.9725289 #> May 2017 1452.400 NA NA 0.9240579 1571.763 1552.613 0.9354553 NaN #> Jun 2017 1557.200 NA NA 0.9906046 1571.969 1554.256 1.0018939 NaN #> Jul 2017 1451.753 NA NA 0.9669725 1501.339 1554.036 0.9301585 0.9341824 #> Aug 2017 1303.100 NA NA 0.8370380 1556.799 1554.112 0.8384851 NaN #> d10 d11 d12 d13 #> Mar 2017 0.8911974 1537.594 1549.121 0.9925591 #> Apr 2017 0.9472835 1607.333 1550.650 1.0365542 #> May 2017 0.9277056 1565.583 1551.579 1.0090258 #> Jun 2017 0.9945226 1565.776 1552.914 1.0082826 #> Jul 2017 0.9598855 1505.909 1552.756 0.9698294 #> Aug 2017 0.8381839 1554.671 1552.438 1.0014382 x11_spec <- set_x11(x11_spec, henderson.filter = 13) x11(y, x11_spec) #> Last values #> d1 d2 d4 d5 d6 d7 d8 d9 #> Mar 2017 1370.300 NA NA 0.8957968 1529.700 1556.611 0.8803097 NaN #> Apr 2017 1521.625 NA NA 0.9391175 1620.271 1562.636 0.9743791 0.9737554 #> May 2017 1452.400 NA NA 0.9239661 1571.919 1563.794 0.9287669 NaN #> Jun 2017 1557.200 NA NA 0.9916495 1570.313 1560.504 0.9978827 NaN #> Jul 2017 1453.790 NA NA 0.9669146 1503.535 1555.034 0.9295618 0.9348928 #> Aug 2017 1303.100 NA NA 0.8369658 1556.933 1547.539 0.8420469 NaN #> d10 d11 d12 d13 #> Mar 2017 0.8899565 1539.738 1556.256 0.9893865 #> Apr 2017 0.9498667 1602.962 1560.758 1.0270404 #> May 2017 0.9267578 1567.184 1560.959 1.0039883 #> Jun 2017 0.9952607 1564.615 1557.551 1.0045354 #> Jul 2017 0.9607704 1504.522 1552.689 0.9689783 #> Aug 2017 0.8393622 1552.488 1545.113 1.0047731"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Set X-11 Specification — set_x11","title":"Set X-11 Specification — set_x11","text":"Set X-11 Specification","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set X-11 Specification — set_x11","text":"","code":"set_x11( x, mode = c(NA, \"Undefined\", \"Additive\", \"Multiplicative\", \"LogAdditive\", \"PseudoAdditive\"), seasonal.comp = NA, seasonal.filter = NA, henderson.filter = NA, lsigma = NA, usigma = NA, fcasts = NA, bcasts = NA, calendar.sigma = c(NA, \"None\", \"Signif\", \"All\", \"Select\"), sigma.vector = NA, exclude.forecast = NA, bias = c(NA, \"LEGACY\") )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set X-11 Specification — set_x11","text":"x specification modified, object class \"JD3_X11_SPEC\", default X11 spec can obtained 'x=x11_spec()' mode character: decomposition mode. Determines mode seasonal adjustment decomposition performed: \"Undefined\" - assumption concerning relationship time series components made; \"Additive\" - assumes additive relationship; \"Multiplicative\" - assumes multiplicative relationship; \"LogAdditive\" - performs additive decomposition logarithms series adjusted; \"PseudoAdditive\" - assumes pseudo-additive relationship. changed program, needed. seasonal.comp logical: TRUE, program computes seasonal component. Otherwise, seasonal component estimated values set 0 (additive decomposition) 1 (multiplicative decomposition). seasonal.filter vector character(s) specifying seasonal moving average (.e. seasonal filter) used estimate seasonal factors entire series. vector can length: 1 - seasonal filter used periods (e.g.: seasonal.filter = \"Msr\" seasonal.filter = \"S3X3\" ); different value quarter (length 4) month (length 12) - (e.g. quarterly series: seasonal.filter = c(\"S3X3\", \"Msr\", \"S3X3\", \"Msr\")). Possible filters : \"Msr\", \"Stable\", \"X11Default\", \"S3X1\", \"S3X3\", \"S3X5\", \"S3X9\", \"S3X15\". \"Msr\" - program chooses final seasonal filter automatically. henderson.filter numeric: length Henderson filter (odd number 3 101). henderson.filter = 0 automatic selection Henderson filter's length trend estimation enabled. lsigma numeric: lower sigma boundary detection extreme values, > 0.5, default=1.5. usigma numeric: upper sigma boundary detection extreme values, > lsigma, default=2.5. bcasts, fcasts numeric: number backasts (bcasts) forecasts (fcasts) generated RegARIMA model periods (positive values) years (negative values).Default values: fcasts=-1 bcasts=0. calendar.sigma character specify standard errors used extreme values detection adjustment computed: 5 year spans irregulars (\"None\", default value); separately calendar period (\"\"); separately period Cochran's hypothesis test determines irregular component heteroskedastic calendar month/quarter (\"Signif\"); separately two complementary sets calendar months/quarters specified x11.sigmaVector parameter (\"Select\", see parameter sigma.vector). sigma.vector vector specify one two groups periods standard errors used extreme values detection adjustment computed separately. used calendar.sigma = \"Select\". Possible values : 1 2. exclude.forecast Boolean exclude forecasts backcasts. TRUE, RegARIMA model forecasts backcasts used detection extreme values seasonal adjustment routines.Default= FALSE. bias TODO.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set X-11 Specification — set_x11","text":"\"JD3_X11_SPEC\" object, containing parameters.","code":""},{"path":[]},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set X-11 Specification — set_x11","text":"","code":"init_spec <- x11_spec() new_spec <- set_x11(init_spec, mode = \"LogAdditive\", seasonal.comp = 1, seasonal.filter = \"S3X9\", henderson.filter = 7, lsigma = 1.7, usigma = 2.7, fcasts = -1, bcasts = -1, calendar.sigma =\"All\", sigma.vector = NA, exclude.forecast = FALSE, bias = \"LEGACY\")"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13.html","id":null,"dir":"Reference","previous_headings":"","what":"Seasonal Adjustment with X13-ARIMA — x13","title":"Seasonal Adjustment with X13-ARIMA — x13","text":"Seasonal Adjustment X13-ARIMA","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Seasonal Adjustment with X13-ARIMA — x13","text":"","code":"x13( ts, spec = c(\"rsa4\", \"rsa0\", \"rsa1\", \"rsa2c\", \"rsa3\", \"rsa5c\"), context = NULL, userdefined = NULL ) x13_fast( ts, spec = c(\"rsa4\", \"rsa0\", \"rsa1\", \"rsa2c\", \"rsa3\", \"rsa5c\"), context = NULL, userdefined = NULL ) jx13( ts, spec = c(\"rsa4\", \"rsa0\", \"rsa1\", \"rsa2c\", \"rsa3\", \"rsa5c\"), context = NULL, userdefined = NULL )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Seasonal Adjustment with X13-ARIMA — x13","text":"ts univariate time series. spec model specification. Can either name predefined specification user-defined specification. context list external regressors (calendar ) used estimation userdefined vector containing additional output variables (see x13_dictionary()).","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Seasonal Adjustment with X13-ARIMA — x13","text":"x13() function returns list results, estimation specification result specification, x13_fast() faster function returns results. jx13() functions returns results java object allow customize outputs packages (use rjd3toolkit::dictionary() get list variables rjd3toolkit::result() get specific variable). estimation functions x13() x13_fast() can directly use specification name (string). want customize specification create specification object first.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Seasonal Adjustment with X13-ARIMA — x13","text":"","code":"y = rjd3toolkit::ABS$X0.2.09.10.M x13_fast(y,\"rsa3\") #> RegARIMA #> Log-transformation: yes #> SARIMA model: (2,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> phi(1) 0.38042 0.06517 5.838 #> phi(2) 0.21398 0.06040 3.543 #> theta(1) -0.70246 0.05146 -13.650 #> btheta(1) -0.56259 0.06648 -8.462 #> #> Regression model: #> Estimate Std. Error T-stat #> TC (2000-06-01) 0.15476 0.03158 4.900 #> AO (2000-07-01) -0.29505 0.04425 -6.668 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 7 #> #> Loglikelihood: 720.2196 #> Adjusted loglikelihood: -2147.407 #> #> Standard error of the regression (ML estimate): 0.04181753 #> AIC: 4308.814 #> AICC: 4309.092 #> BIC: 4336.962 #> #> #> Decomposition #> Monitoring and Quality Assessment Statistics: #> M stats #> m1 0.095 #> m2 0.097 #> m3 2.384 #> m4 0.403 #> m5 1.940 #> m6 0.179 #> m7 0.053 #> m8 0.176 #> m9 0.047 #> m10 0.157 #> m11 0.154 #> q 0.560 #> qm2 0.617 #> #> Final filters: #> Seasonal filter: #> Trend filter: 23 terms Henderson moving average #> #> Diagnostics #> Relative contribution of the components to the stationary #> portion of the variance in the original series, #> after the removal of the long term trend (in %) #> #> Component #> cycle 13.259 #> seasonal 86.854 #> irregular 0.970 #> calendar 0.000 #> others 0.299 #> total 101.383 #> #> Residual seasonality tests #> P.value #> seas.ftest.i 0.994 #> seas.ftest.sa 0.999 #> seas.qstest.i 0.045 #> seas.qstest.sa 0.035 #> td.ftest.i 0.000 #> td.ftest.sa 0.000 #> #> #> Final #> Last values #> series sa trend seas irr #> Sep 2016 1393.5 1558.700 1547.466 0.8940140 1.0072601 #> Oct 2016 1497.4 1539.619 1543.359 0.9725785 0.9975766 #> Nov 2016 1684.3 1540.423 1541.287 1.0934011 0.9994390 #> Dec 2016 2850.4 1561.106 1541.060 1.8258848 1.0130078 #> Jan 2017 1428.5 1513.071 1542.061 0.9441065 0.9812005 #> Feb 2017 1092.4 1538.709 1543.697 0.7099456 0.9967689 #> Mar 2017 1370.3 1532.681 1545.484 0.8940545 0.9917157 #> Apr 2017 1522.6 1609.073 1547.232 0.9462594 1.0399684 #> May 2017 1452.4 1563.830 1548.782 0.9287456 1.0097158 #> Jun 2017 1557.2 1561.361 1549.791 0.9973351 1.0074658 #> Jul 2017 1445.5 1519.197 1550.206 0.9514898 0.9799965 #> Aug 2017 1303.1 1554.064 1550.306 0.8385112 1.0024242 x13(y,\"rsa5c\") #> RegARIMA #> Log-transformation: yes #> SARIMA model: (0,1,1) (1,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> theta(1) -0.81551 0.06932 -11.76 #> bphi(1) -0.43410 0.02650 -16.38 #> btheta(1) -0.82463 0.04458 -18.50 #> #> Regression model: #> Estimate Std. Error T-stat #> monday -0.009156 0.003278 -2.793 #> tuesday 0.004523 0.003316 1.364 #> wednesday -0.001181 0.003285 -0.360 #> thursday 0.013349 0.003304 4.040 #> friday -0.001501 0.003313 -0.453 #> saturday 0.014993 0.003293 4.553 #> easter 0.051061 0.006629 7.703 #> TC (2000-06-01) 0.162337 0.026546 6.115 #> AO (2000-07-01) -0.306371 0.032121 -9.538 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 13 #> #> Loglikelihood: 794.8103 #> Adjusted loglikelihood: -2072.816 #> #> Standard error of the regression (ML estimate): 0.03486636 #> AIC: 4171.633 #> AICC: 4172.548 #> BIC: 4223.906 #> #> #> Decomposition #> Monitoring and Quality Assessment Statistics: #> M stats #> m1 0.054 #> m2 0.049 #> m3 1.765 #> m4 0.089 #> m5 1.416 #> m6 0.012 #> m7 0.052 #> m8 0.160 #> m9 0.050 #> m10 0.124 #> m11 0.122 #> q 0.385 #> qm2 0.427 #> #> Final filters: #> Seasonal filter: #> Trend filter: 23 terms Henderson moving average #> #> Diagnostics #> Relative contribution of the components to the stationary #> portion of the variance in the original series, #> after the removal of the long term trend (in %) #> #> Component #> cycle 13.437 #> seasonal 86.536 #> irregular 0.489 #> calendar 0.599 #> others 0.323 #> total 101.383 #> #> Residual seasonality tests #> P.value #> seas.ftest.i 0.977 #> seas.ftest.sa 0.986 #> seas.qstest.i 0.010 #> seas.qstest.sa 0.004 #> td.ftest.i 0.993 #> td.ftest.sa 0.996 #> #> #> Final #> Last values #> series sa trend seas irr #> Sep 2016 1393.5 1544.956 1548.898 0.9019675 0.9974547 #> Oct 2016 1497.4 1569.404 1547.139 0.9541202 1.0143910 #> Nov 2016 1684.3 1523.294 1546.637 1.1056957 0.9849077 #> Dec 2016 2850.4 1536.881 1546.937 1.8546651 0.9934994 #> Jan 2017 1428.5 1547.497 1547.656 0.9231038 0.9998970 #> Feb 2017 1092.4 1545.588 1548.350 0.7067860 0.9982164 #> Mar 2017 1370.3 1556.927 1548.747 0.8801312 1.0052818 #> Apr 2017 1522.6 1587.924 1548.518 0.9588622 1.0254473 #> May 2017 1452.4 1555.232 1547.335 0.9338798 1.0051038 #> Jun 2017 1557.2 1539.880 1545.271 1.0112477 0.9965115 #> Jul 2017 1445.5 1532.110 1542.807 0.9434701 0.9930664 #> Aug 2017 1303.1 1524.546 1540.399 0.8547462 0.9897086 regarima_fast(y,\"rg0\") #> Log-transformation: no #> SARIMA model: (0,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> theta(1) -0.87640 0.02290 -38.275 #> btheta(1) -0.38755 0.05071 -7.642 #> #> No regression variables #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 3 #> #> Loglikelihood: -2218.964 #> Standard error of the regression (ML estimate): 52.59136 #> AIC: 4443.928 #> AICC: 4443.987 #> BIC: 4455.991 #> regarima(y,\"rg3\") #> Log-transformation: yes #> SARIMA model: (2,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> phi(1) 0.38042 0.06517 5.838 #> phi(2) 0.21398 0.06040 3.543 #> theta(1) -0.70246 0.05146 -13.650 #> btheta(1) -0.56259 0.06648 -8.462 #> #> Regression model: #> Estimate Std. Error T-stat #> TC (2000-06-01) 0.15476 0.03158 4.900 #> AO (2000-07-01) -0.29505 0.04425 -6.668 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 7 #> #> Loglikelihood: 720.2196 #> Adjusted loglikelihood: -2147.407 #> #> Standard error of the regression (ML estimate): 0.04181753 #> AIC: 4308.814 #> AICC: 4309.092 #> BIC: 4336.962 #> sp = x13_spec(\"rsa5c\") sp = rjd3toolkit::add_outlier(sp, type = c(\"AO\"), c(\"2015-01-01\", \"2010-01-01\")) sp = rjd3toolkit::set_transform( rjd3toolkit::set_tradingdays( rjd3toolkit::set_easter(sp, enabled = FALSE), option = \"workingdays\" ), fun = \"None\" ) x13(y,spec=sp) #> RegARIMA #> Log-transformation: no #> SARIMA model: (3,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> phi(1) 0.16557 0.06461 2.563 #> phi(2) 0.08780 0.06435 1.364 #> phi(3) -0.11287 0.06047 -1.867 #> theta(1) -0.86076 0.03927 -21.918 #> btheta(1) -0.22918 0.05193 -4.413 #> #> Regression model: #> Estimate Std. Error T-stat #> td 1.0015 0.8098 1.237 #> lp 29.6392 11.5469 2.567 #> AO (2010-01-01) 37.0476 34.4300 1.076 #> AO (2015-01-01) 27.9946 35.0234 0.799 #> AO (2000-06-01) 199.7536 34.5756 5.777 #> AO (2000-07-01) -194.6887 34.6240 -5.623 #> LS (2005-04-01) -82.3062 17.4210 -4.725 #> LS (2015-07-01) 81.3334 18.0924 4.495 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 14 #> #> Loglikelihood: -2159.902 #> Standard error of the regression (ML estimate): 45.632 #> AIC: 4347.804 #> AICC: 4348.862 #> BIC: 4404.099 #> #> #> Decomposition #> Monitoring and Quality Assessment Statistics: #> M stats #> m1 0.043 #> m2 0.033 #> m3 1.909 #> m4 0.447 #> m5 1.607 #> m6 0.399 #> m7 0.167 #> m8 0.298 #> m9 0.145 #> m10 0.308 #> m11 0.293 #> q 0.552 #> qm2 0.624 #> #> Final filters: #> Seasonal filter: #> Trend filter: 23 terms Henderson moving average #> #> Diagnostics #> Relative contribution of the components to the stationary #> portion of the variance in the original series, #> after the removal of the long term trend (in %) #> #> Component #> cycle 6.178 #> seasonal 94.402 #> irregular 0.325 #> calendar 0.020 #> others 1.504 #> total 102.429 #> #> Residual seasonality tests #> P.value #> seas.ftest.i 0.902 #> seas.ftest.sa 0.996 #> seas.qstest.i 0.038 #> seas.qstest.sa 0.138 #> td.ftest.i 0.000 #> td.ftest.sa 0.000 #> #> #> Final #> Last values #> series sa trend seas irr #> Sep 2016 1393.5 1567.439 1554.386 -173.938673 13.052790 #> Oct 2016 1497.4 1547.052 1551.116 -49.651713 -4.064205 #> Nov 2016 1684.3 1544.888 1549.070 139.411575 -4.182012 #> Dec 2016 2850.4 1560.255 1548.078 1290.145096 12.177219 #> Jan 2017 1428.5 1529.584 1547.770 -101.084412 -18.185622 #> Feb 2017 1092.4 1552.751 1547.915 -460.350943 4.835817 #> Mar 2017 1370.3 1532.112 1548.227 -161.812246 -16.114386 #> Apr 2017 1522.6 1571.429 1548.386 -48.829077 23.042779 #> May 2017 1452.4 1554.351 1548.282 -101.951014 6.069336 #> Jun 2017 1557.2 1548.139 1547.682 9.061423 0.456243 #> Jul 2017 1445.5 1540.321 1546.422 -94.820887 -6.101117 #> Aug 2017 1303.1 1554.506 1544.648 -251.405622 9.857735 sp = set_x11(sp, henderson.filter = 13) x13_fast(y, spec = sp) #> RegARIMA #> Log-transformation: no #> SARIMA model: (3,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> phi(1) 0.16557 0.06461 2.563 #> phi(2) 0.08780 0.06435 1.364 #> phi(3) -0.11287 0.06047 -1.867 #> theta(1) -0.86076 0.03927 -21.918 #> btheta(1) -0.22918 0.05193 -4.413 #> #> Regression model: #> Estimate Std. Error T-stat #> td 1.0015 0.8098 1.237 #> lp 29.6392 11.5469 2.567 #> AO (2010-01-01) 37.0476 34.4300 1.076 #> AO (2015-01-01) 27.9946 35.0234 0.799 #> AO (2000-06-01) 199.7536 34.5756 5.777 #> AO (2000-07-01) -194.6887 34.6240 -5.623 #> LS (2005-04-01) -82.3062 17.4210 -4.725 #> LS (2015-07-01) 81.3334 18.0924 4.495 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 14 #> #> Loglikelihood: -2159.902 #> Standard error of the regression (ML estimate): 45.632 #> AIC: 4347.804 #> AICC: 4348.862 #> BIC: 4404.099 #> #> #> Decomposition #> Monitoring and Quality Assessment Statistics: #> M stats #> m1 0.036 #> m2 0.029 #> m3 1.786 #> m4 0.537 #> m5 1.528 #> m6 0.449 #> m7 0.167 #> m8 0.290 #> m9 0.144 #> m10 0.297 #> m11 0.285 #> q 0.533 #> qm2 0.604 #> #> Final filters: #> Seasonal filter: #> Trend filter: 13 terms Henderson moving average #> #> Diagnostics #> Relative contribution of the components to the stationary #> portion of the variance in the original series, #> after the removal of the long term trend (in %) #> #> Component #> cycle 6.227 #> seasonal 94.535 #> irregular 0.290 #> calendar 0.020 #> others 1.506 #> total 102.578 #> #> Residual seasonality tests #> P.value #> seas.ftest.i 0.924 #> seas.ftest.sa 0.997 #> seas.qstest.i 0.018 #> seas.qstest.sa 0.117 #> td.ftest.i 0.000 #> td.ftest.sa 0.000 #> #> #> Final #> Last values #> series sa trend seas irr #> Sep 2016 1393.5 1567.255 1554.069 -173.754955 13.186079 #> Oct 2016 1497.4 1547.824 1551.331 -50.424459 -3.506850 #> Nov 2016 1684.3 1546.128 1548.057 138.172189 -1.929197 #> Dec 2016 2850.4 1557.769 1545.941 1292.630528 11.828053 #> Jan 2017 1428.5 1530.142 1545.777 -101.642006 -15.634829 #> Feb 2017 1092.4 1552.860 1546.573 -460.459760 6.287131 #> Mar 2017 1370.3 1532.029 1548.853 -161.728778 -16.824581 #> Apr 2017 1522.6 1571.073 1551.472 -48.473184 19.601512 #> May 2017 1452.4 1554.849 1552.698 -102.448692 2.151163 #> Jun 2017 1557.2 1550.052 1552.154 7.148413 -2.102681 #> Jul 2017 1445.5 1541.002 1549.653 -95.502435 -8.650144 #> Aug 2017 1303.1 1554.802 1545.625 -251.702019 9.176569"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_dictionary.html","id":null,"dir":"Reference","previous_headings":"","what":"X-13 Dictionary — x13_dictionary","title":"X-13 Dictionary — x13_dictionary","text":"X-13 Dictionary","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_dictionary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"X-13 Dictionary — x13_dictionary","text":"","code":"x13_dictionary()"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_dictionary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"X-13 Dictionary — x13_dictionary","text":"vector containing names available output objects (series, diagnostics, parameters).","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_revisions.html","id":null,"dir":"Reference","previous_headings":"","what":"Revisions History — x13_revisions","title":"Revisions History — x13_revisions","text":"Compute revisions history","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_revisions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Revisions History — x13_revisions","text":"","code":"x13_revisions( ts, spec, data_ids = NULL, ts_ids = NULL, cmp_ids = NULL, context = NULL )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_revisions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Revisions History — x13_revisions","text":"ts time series used estimation. spec specification used. data_ids list list specify statistics export. sub-list must contain two elements: start (first date compute history, format \"YYYY-MM-DD\") id (name statistics, see x13_dictionary()). See example. ts_ids list list specify specific date component whose history studied. sub-list must contain three elements: start (first date compute history, format \"YYYY-MM-DD\"), period (date studied) id (name component, see x13_dictionary()). See example. cmp_ids list list specify component whose history studied. sub-list must contain three elements: start (first date compute history, format \"YYYY-MM-DD\"), end (last date compute history, format \"YYYY-MM-DD\") id (name component, see x13_dictionary()). many series periods start end exported. See example. context context specification.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_revisions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Revisions History — x13_revisions","text":"","code":"s <- rjd3toolkit::ABS$X0.2.09.10.M sa_mod <- x13(s) data_ids <- list( # Get the coefficient of the trading-day coefficient from 2005-jan list(start = \"2005-01-01\", id = \"regression.td(1)\"), # Get the ljung-box statistics on residuals from 2010-jan list(start = \"2010-01-01\", id = \"residuals.lb\")) ts_ids <- list( # Get the SA component estimates of 2010-jan from 2010-jan list(period = \"2010-01-01\", start = \"2010-01-01\", id = \"sa\"), # Get the irregular component estimates of 2010-jan from 2015-jan list(period = \"2010-01-01\", start = \"2015-01-01\", id = \"i\")) cmp_ids <- list( # Get the SA component estimates (full time series) 2010-jan to 2020-jan list(start = \"2010-01-01\", end = \"2020-01-01\", id = \"sa\"), # Get the trend component estimates (full time series) 2010-jan to 2020-jan list(start = \"2010-01-01\", end = \"2020-01-01\", id = \"t\")) rh <- x13_revisions(s, sa_mod$result_spec, data_ids, ts_ids, cmp_ids)"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"RegARIMA/X-13 Default Specifications — x13_spec","title":"RegARIMA/X-13 Default Specifications — x13_spec","text":"Set functions create default specification objects associated X-13ARIMA seasonal adjustment method.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RegARIMA/X-13 Default Specifications — x13_spec","text":"","code":"regarima_spec(name = c(\"rg4\", \"rg0\", \"rg1\", \"rg2c\", \"rg3\", \"rg5c\")) x13_spec(name = c(\"rsa4\", \"rsa0\", \"rsa1\", \"rsa2c\", \"rsa3\", \"rsa5c\")) x11_spec()"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RegARIMA/X-13 Default Specifications — x13_spec","text":"name name predefined specification.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RegARIMA/X-13 Default Specifications — x13_spec","text":"object class \"JD3_X13_SPEC\" (x13_spec()), \"JD3_REGARIMA_SPEC\" (regarima_spec()) \"JD3_X11_SPEC\" (x11_spec()).","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_spec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RegARIMA/X-13 Default Specifications — x13_spec","text":"Specification setting sheer X-11 decomposition method (without reg-arima pre-adjustment) supported x11_spec() function appear among possible X13-Arima default specifications Specification setting can restricted reg-arima part regarima_spec() function, without argument regarima_spec() yields RG5c specification setting complete X13-Arima spec, x13_spec() without argument yields RSA5c specification available predefined 'JDemetra+' model specifications described table : #' @seealso set pre-processing parameters: rjd3toolkit::set_arima(), rjd3toolkit::set_automodel(), rjd3toolkit::set_basic(), rjd3toolkit::set_easter(), rjd3toolkit::set_estimate(), rjd3toolkit::set_outlier(), rjd3toolkit::set_tradingdays(), rjd3toolkit::set_transform(), rjd3toolkit::add_outlier(), rjd3toolkit::remove_outlier(), rjd3toolkit::add_ramp(), rjd3toolkit::remove_ramp(), rjd3toolkit::add_usrdefvar(). set decomposition parameters: set_x11(). set benchmarking parameters: rjd3toolkit::set_benchmarking().","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RegARIMA/X-13 Default Specifications — x13_spec","text":"","code":"init_spec <- x11_spec() init_spec <- regarima_spec(\"rg4\") init_spec <- x13_spec(\"rsa5c\")"}]
+[{"path":"https://rjdemetra.github.io/rjd3x13/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Jean Palate. Author, maintainer. Alain Quartier-la-Tente. Author.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Palate J, Quartier-la-Tente (2024). rjd3x13: Seasonal Adjustment X-13 'JDemetra+ 3.0'. R package version 3.2.2, https://rjdemetra.github.io/rjd3x13/, https://github.com/rjdemetra/rjd3x13.","code":"@Manual{, title = {rjd3x13: Seasonal Adjustment with X-13 and 'JDemetra+ 3.0'}, author = {Jean Palate and Alain Quartier-la-Tente}, year = {2024}, note = {R package version 3.2.2, https://rjdemetra.github.io/rjd3x13/}, url = {https://github.com/rjdemetra/rjd3x13}, }"},{"path":"https://rjdemetra.github.io/rjd3x13/index.html","id":"rjd3x13","dir":"","previous_headings":"","what":"Seasonal Adjustment with X-13 and JDemetra+ 3.0","title":"Seasonal Adjustment with X-13 and JDemetra+ 3.0","text":"rjd3x13 offers full acces options outputs X-13-Arima (rjd3x13::x13()), including RegARIMA modelling (rjd3x13::regarima()) X-11 decomposition (rjd3x13::x11()). specification can created functions rjd3x13::regarima_spec(), rjd3x13::x11_spec() rjd3x13::x13_spec() can modified function: pre-processing: rjd3toolkit::set_arima(), rjd3toolkit::set_automodel(), rjd3toolkit::set_basic(), rjd3toolkit::set_easter(), rjd3toolkit::set_estimate(), rjd3toolkit::set_outlier(), rjd3toolkit::set_tradingdays(), rjd3toolkit::set_transform(), rjd3toolkit::add_outlier(), rjd3toolkit::remove_outlier(), rjd3toolkit::add_ramp(), rjd3toolkit::remove_ramp(), rjd3toolkit::add_usrdefvar(); decomposition: rjd3x13::set_x11(); benchmarking: rjd3toolkit::set_benchmarking().","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Seasonal Adjustment with X-13 and JDemetra+ 3.0","text":"get current stable version (latest release): get current development version GitHub:","code":"# install.packages(\"remotes\") remotes::install_github(\"rjdemetra/rjd3x13@*release\") # install.packages(\"remotes\") remotes::install_github(\"rjdemetra/rjd3x13\")"},{"path":"https://rjdemetra.github.io/rjd3x13/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Seasonal Adjustment with X-13 and JDemetra+ 3.0","text":"","code":"library(\"rjd3x13\") y <- rjd3toolkit::ABS$X0.2.09.10.M x13_model <- x13(y) summary(x13_model$result$preprocessing) # Summary of regarima model #> Log-transformation: yes #> SARIMA model: (2,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat Pr(>|t|) #> phi(1) 0.34740 0.06502 5.343 1.53e-07 *** #> phi(2) 0.21733 0.06000 3.622 0.000329 *** #> theta(1) -0.69937 0.05115 -13.672 < 2e-16 *** #> btheta(1) -0.48038 0.06993 -6.869 2.45e-11 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Regression model: #> Estimate Std. Error T-stat Pr(>|t|) #> td 0.0023233 0.0006844 3.395 0.000755 *** #> easter 0.0520113 0.0084894 6.127 2.13e-09 *** #> TC (2000-06-01) 0.1590340 0.0288578 5.511 6.37e-08 *** #> AO (2000-07-01) -0.2900774 0.0400551 -7.242 2.25e-12 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> Number of observations: 425 , Number of effective observations: 412 , Number of parameters: 9 #> Loglikelihood: 746.7517, Adjusted loglikelihood: -2120.875 #> Standard error of the regression (ML estimate): 0.03927991 #> AIC: 4259.75 , AICc: 4260.198 , BIC: 4295.939 plot(x13_model) # Plot of the final decomposition"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/deprecated-rjd3x13.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated functions — deprecated-rjd3x13","title":"Deprecated functions — deprecated-rjd3x13","text":"Deprecated functions","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/deprecated-rjd3x13.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated functions — deprecated-rjd3x13","text":"","code":"spec_x13(name = c(\"rsa4\", \"rsa0\", \"rsa1\", \"rsa2c\", \"rsa3\", \"rsa5c\")) spec_regarima(name = c(\"rg4\", \"rg0\", \"rg1\", \"rg2c\", \"rg3\", \"rg5c\")) spec_x11() fast_x13( ts, spec = c(\"rsa4\", \"rsa0\", \"rsa1\", \"rsa2c\", \"rsa3\", \"rsa5c\"), context = NULL, userdefined = NULL ) fast_regarima( ts, spec = c(\"rg4\", \"rg0\", \"rg1\", \"rg2c\", \"rg3\", \"rg5c\"), context = NULL, userdefined = NULL )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/deprecated-rjd3x13.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deprecated functions — deprecated-rjd3x13","text":"ts, spec, context, userdefined, name Parameters.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/jd3_utilities.html","id":null,"dir":"Reference","previous_headings":"","what":"Java Utility Functions — jd3_utilities","title":"Java Utility Functions — jd3_utilities","text":"functions used JDemetra+ 3.0 packages easily interact R Java objects.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/jd3_utilities.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Java Utility Functions — jd3_utilities","text":"","code":".x13_rslts(jrslts) .jd2r_spec_x11(jspec) .r2jd_spec_x11(spec) .r2jd_spec_x13(spec) .jd2r_spec_x13(jspec)"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/jd3_utilities.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Java Utility Functions — jd3_utilities","text":"spec, jspec, jrslts parameters.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":null,"dir":"Reference","previous_headings":"","what":"Refresh a specification with constraints — refresh","title":"Refresh a specification with constraints — refresh","text":"Function allowing create new specification updating specification used previous estimation. selected parameters kept fixed (previous estimation results) others freed re-estimation domain constraints. See details examples.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Refresh a specification with constraints — refresh","text":"","code":"regarima_refresh( spec, refspec = NULL, policy = c(\"FreeParameters\", \"Complete\", \"Outliers_StochasticComponent\", \"Outliers\", \"FixedParameters\", \"FixedAutoRegressiveParameters\", \"Fixed\", \"Current\"), period = 0, start = NULL, end = NULL ) x13_refresh( spec, refspec = NULL, policy = c(\"FreeParameters\", \"Complete\", \"Outliers_StochasticComponent\", \"Outliers\", \"FixedParameters\", \"FixedAutoRegressiveParameters\", \"Fixed\", \"Current\"), period = 0, start = NULL, end = NULL )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Refresh a specification with constraints — refresh","text":"spec current specification refreshed (\"result_spec\"). refspec reference specification used define domain considered re-estimation (\"domain_spec\"). default \"RG5c\" \"RSA5\" specification. policy refresh policy apply (see details). period, start, end specify span outliers re-identified (.e.: re-detected) policy = \"Outliers\" policy = \"Outliers_StochasticComponent\". Span definition: period: numeric, number observations year (12, 4...). start end: first last date outliers re-identfied, defined arrays two elements: year first period (example, period = 12, c(1980, 1) January 1980). specified, outliers re-identified whole series.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Refresh a specification with constraints — refresh","text":"new specification, object class \"JD3_X13_SPEC\" \"JD3_REGARIMA_SPEC\".","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Refresh a specification with constraints — refresh","text":"selection constraints kept fixed re-estimated called revision policy. User-defined parameters always copied new refreshed specifications. X-13 reg-arima part can refreshed. X-11 decomposition completely re-run, keeping user-defined parameters original specification. Available refresh policies : Current: applying current pre-adjustment reg-arima model adding new raw data points Additive Outliers (defined new intervention variables) Fixed: applying current pre-adjustment reg-arima model replacing forecasts new raw data points. FixedParameters: pre-adjustment reg-arima model partially modified: regression coefficients re-estimated regression variables, Arima orders coefficients unchanged. FixedAutoRegressiveParameters: FixedParameters Arima Moving Average coefficients (MA) also re-estimated, Auto-regressive (AR) coefficients kept fixed. FreeParameters: regression Arima model coefficients re-estimated, regression variables Arima orders kept fixed. Outliers: regression variables Arima orders kept fixed, outliers re-detected defined span, thus regression Arima model coefficients re-estimated Outliers_StochasticComponent: \"Outliers\" Arima model orders (p,d,q)(P,D,Q) can also re-identified.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Refresh a specification with constraints — refresh","text":"information revision policies JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/t-rev-policies-production","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/refresh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Refresh a specification with constraints — refresh","text":"","code":"y<- rjd3toolkit::ABS$X0.2.08.10.M # raw series for first estimation y_raw <-window(y,end = 2009) # raw series for second (refreshed) estimation y_new <-window(y,end = 2010) # specification for first estimation spec_x13_1<-x13_spec(\"rsa5c\") # first estimation sa_x13<- x13(y_raw, spec_x13_1) # refreshing the specification current_result_spec <- sa_x13$result_spec current_domain_spec <- sa_x13$estimation_spec spec_x13_ref <- x13_refresh(current_result_spec, # point spec to be refreshed current_domain_spec, #domain spec (set of constraints) policy = \"Fixed\") # 2nd estimation with refreshed specification sa_x13_ref <- x13(y_new, spec_x13_ref)"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima.html","id":null,"dir":"Reference","previous_headings":"","what":"RegARIMA model, pre-adjustment in X13 — regarima","title":"RegARIMA model, pre-adjustment in X13 — regarima","text":"RegARIMA model, pre-adjustment X13","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RegARIMA model, pre-adjustment in X13 — regarima","text":"","code":"regarima( ts, spec = c(\"rg4\", \"rg0\", \"rg1\", \"rg2c\", \"rg3\", \"rg5c\"), context = NULL, userdefined = NULL ) regarima_fast( ts, spec = c(\"rg4\", \"rg0\", \"rg1\", \"rg2c\", \"rg3\", \"rg5c\"), context = NULL, userdefined = NULL )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RegARIMA model, pre-adjustment in X13 — regarima","text":"ts univariate time series. spec model specification. Can either name predefined specification user-defined specification. context list external regressors (calendar ) used estimation userdefined vector containing additional output variables (see x13_dictionary()).","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RegARIMA model, pre-adjustment in X13 — regarima","text":"regarima() function returns list results (\"JD3_REGARIMA_RSLTS\" object), estimation specification result specification, regarima_fast() faster function returns results.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RegARIMA model, pre-adjustment in X13 — regarima","text":"","code":"y = rjd3toolkit::ABS$X0.2.09.10.M sp = regarima_spec(\"rg5c\") sp = rjd3toolkit::add_outlier(sp, type = c(\"AO\"), c(\"2015-01-01\", \"2010-01-01\")) regarima_fast(y, spec = sp) #> Log-transformation: yes #> SARIMA model: (0,1,1) (1,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> theta(1) -0.81606 0.06959 -11.73 #> bphi(1) -0.43734 0.02661 -16.44 #> btheta(1) -0.82509 0.04481 -18.41 #> #> Regression model: #> Estimate Std. Error T-stat #> monday -0.008747 0.003287 -2.661 #> tuesday 0.004488 0.003314 1.354 #> wednesday -0.001471 0.003294 -0.447 #> thursday 0.013886 0.003325 4.176 #> friday -0.001944 0.003325 -0.585 #> saturday 0.015368 0.003304 4.651 #> easter 0.051130 0.006621 7.723 #> AO (2010-01-01) 0.035349 0.028796 1.228 #> AO (2015-01-01) -0.020385 0.028885 -0.706 #> TC (2000-06-01) 0.162169 0.026510 6.117 #> AO (2000-07-01) -0.306536 0.032095 -9.551 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 15 #> #> Loglikelihood: 795.8429 #> Adjusted loglikelihood: -2071.784 #> #> Standard error of the regression (ML estimate): 0.03478056 #> AIC: 4173.568 #> AICC: 4174.78 #> BIC: 4233.883 #> sp = rjd3toolkit::set_transform( rjd3toolkit::set_tradingdays( rjd3toolkit::set_easter(sp, enabled = FALSE), option = \"workingdays\" ), fun = \"None\" ) regarima_fast(y, spec = sp) #> Log-transformation: no #> SARIMA model: (3,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> phi(1) 0.16557 0.06461 2.563 #> phi(2) 0.08780 0.06435 1.364 #> phi(3) -0.11287 0.06047 -1.867 #> theta(1) -0.86076 0.03927 -21.918 #> btheta(1) -0.22918 0.05193 -4.413 #> #> Regression model: #> Estimate Std. Error T-stat #> td 1.0015 0.8098 1.237 #> lp 29.6392 11.5469 2.567 #> AO (2010-01-01) 37.0476 34.4300 1.076 #> AO (2015-01-01) 27.9946 35.0234 0.799 #> AO (2000-06-01) 199.7536 34.5756 5.777 #> AO (2000-07-01) -194.6887 34.6240 -5.623 #> LS (2005-04-01) -82.3062 17.4210 -4.725 #> LS (2015-07-01) 81.3334 18.0924 4.495 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 14 #> #> Loglikelihood: -2159.902 #> Standard error of the regression (ML estimate): 45.632 #> AIC: 4347.804 #> AICC: 4348.862 #> BIC: 4404.099 #> sp = rjd3toolkit::set_outlier(sp, outliers.type = c(\"AO\")) regarima_fast(y, spec = sp) #> Log-transformation: no #> SARIMA model: (3,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> phi(1) 0.11808 0.09633 1.226 #> phi(2) 0.03364 0.09199 0.366 #> phi(3) -0.15061 0.08002 -1.882 #> theta(1) -0.83611 0.07714 -10.838 #> btheta(1) -0.24114 0.05412 -4.456 #> #> Regression model: #> Estimate Std. Error T-stat #> td 1.0012 0.8028 1.247 #> lp 30.8981 11.7820 2.622 #> AO (2010-01-01) 37.2796 35.1800 1.060 #> AO (2015-01-01) 6.5622 35.1307 0.187 #> AO (2000-06-01) 194.6157 35.1753 5.533 #> AO (2000-07-01) -201.1923 35.2286 -5.711 #> AO (2005-04-01) -150.2768 35.1047 -4.281 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 13 #> #> Loglikelihood: -2169.985 #> Standard error of the regression (ML estimate): 46.77874 #> AIC: 4365.971 #> AICC: 4366.885 #> BIC: 4418.244 #>"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"Outlier Detection with a RegARIMA Model — regarima_outliers","title":"Outlier Detection with a RegARIMA Model — regarima_outliers","text":"Outlier Detection RegARIMA Model","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Outlier Detection with a RegARIMA Model — regarima_outliers","text":"","code":"regarima_outliers( y, order = c(0L, 1L, 1L), seasonal = c(0L, 1L, 1L), mean = F, X = NULL, X.td = NULL, ao = T, ls = T, tc = F, so = F, cv = 0, clean = F )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Outlier Detection with a RegARIMA Model — regarima_outliers","text":"y dependent variable (ts object). order, seasonal orders ARIMA model. mean Boolean include mean. X user defined regressors (calendar). X.td calendar regressors. ao, ls, , tc Boolean indicate type outliers detected. cv numeric. entered critical value outlier detection procedure. equal 0 critical value outlier detection procedure automatically determined number observations. clean Clean missing values beginning/end series. Regression variables automatically resized, need .","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Outlier Detection with a RegARIMA Model — regarima_outliers","text":"\"JD3_REGARIMA_OUTLIERS\" object, containing input variables results","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/regarima_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Outlier Detection with a RegARIMA Model — regarima_outliers","text":"","code":"regarima_outliers(rjd3toolkit::ABS$X0.2.09.10.M) #> $model #> $model$y #> Jan Feb Mar Apr May Jun Jul Aug Sep Oct #> 1982 460.1 502.6 443.8 459.1 438.4 465.1 452.7 #> 1983 379.2 378.0 472.1 503.4 510.6 462.4 468.3 458.2 482.7 485.3 #> 1984 414.7 414.5 484.7 487.3 597.9 500.4 543.4 503.4 522.8 556.6 #> 1985 516.3 452.5 525.8 587.7 700.3 561.8 602.8 582.5 563.1 637.1 #> 1986 570.5 478.2 547.4 594.3 751.6 553.4 663.2 581.1 661.9 665.6 #> 1987 613.9 513.2 599.9 674.1 714.0 670.5 720.9 601.6 672.3 709.1 #> 1988 631.0 551.1 678.1 715.7 740.8 722.0 683.5 650.9 723.3 729.6 #> 1989 631.5 552.0 719.0 697.6 764.8 786.3 715.1 723.8 757.9 751.7 #> 1990 678.2 586.2 726.8 744.1 815.5 832.4 710.3 759.4 741.1 786.6 #> 1991 694.0 604.7 719.2 748.2 828.2 746.9 794.5 770.4 741.5 858.6 #> 1992 740.0 665.9 701.5 831.4 878.6 826.0 788.2 723.6 819.8 902.5 #> 1993 762.1 643.0 754.1 840.7 906.6 887.1 771.5 728.7 844.7 886.9 #> 1994 745.7 664.4 821.5 831.7 908.0 912.6 782.9 798.8 887.0 934.6 #> 1995 752.4 682.5 811.2 906.0 927.2 906.8 880.6 873.9 856.8 920.6 #> 1996 833.1 737.1 812.0 895.2 962.8 908.6 908.0 888.9 833.7 933.7 #> 1997 840.9 727.4 857.9 849.0 994.8 830.2 971.1 836.0 939.1 976.9 #> 1998 917.3 716.2 822.9 970.1 970.2 849.4 1042.3 869.9 939.4 1021.3 #> 1999 942.0 738.4 903.2 953.2 1011.2 894.4 1054.5 899.5 1002.3 1043.7 #> 2000 924.9 798.2 901.9 1024.7 1052.3 1165.5 859.3 1009.2 1054.6 1070.4 #> 2001 971.9 814.6 1017.5 1039.2 1123.5 1024.9 1100.8 963.0 1012.9 1132.0 #> 2002 1027.9 841.4 1043.9 1075.3 1190.9 1143.0 1075.7 1065.9 1060.1 1211.4 #> 2003 1099.3 900.5 1092.7 1222.4 1237.1 1237.9 1182.0 1101.2 1198.2 1316.1 #> 2004 1182.9 989.8 1131.4 1277.1 1280.3 1384.1 1305.9 1166.8 1317.9 1358.3 #> 2005 1246.3 1037.3 1300.8 1153.7 1264.2 1454.2 1290.1 1210.7 1277.8 1314.4 #> 2006 1193.7 1037.7 1204.5 1348.6 1267.6 1429.0 1412.0 1239.2 1219.1 1344.6 #> 2007 1267.3 1047.0 1331.6 1302.6 1365.1 1491.5 1462.3 1315.5 1353.3 1440.6 #> 2008 1397.8 1140.5 1351.7 1396.6 1421.1 1401.6 1582.3 1268.4 1383.3 1452.4 #> 2009 1451.0 1056.6 1386.9 1509.1 1519.4 1500.5 1570.7 1341.5 1399.9 1534.3 #> 2010 1469.1 1111.9 1379.9 1389.7 1427.2 1551.4 1581.0 1324.0 1422.0 1464.9 #> 2011 1412.6 1117.5 1321.6 1472.6 1408.9 1471.9 1532.5 1293.5 1345.7 1404.7 #> 2012 1362.4 1131.7 1349.2 1391.2 1456.9 1616.4 1423.4 1359.0 1367.8 1442.6 #> 2013 1397.4 1113.6 1397.3 1339.1 1441.9 1537.4 1390.6 1337.2 1359.4 1463.3 #> 2014 1451.0 1064.9 1293.2 1442.9 1411.8 1461.6 1501.6 1254.2 1356.4 1478.7 #> 2015 1471.2 1053.8 1367.2 1442.2 1428.7 1480.9 1540.9 1331.9 1400.1 1566.3 #> 2016 1519.2 1155.8 1451.5 1451.0 1449.7 1596.1 1468.3 1293.9 1393.5 1497.4 #> 2017 1428.5 1092.4 1370.3 1522.6 1452.4 1557.2 1445.5 1303.1 #> Nov Dec #> 1982 522.9 889.3 #> 1983 568.7 963.7 #> 1984 623.2 1039.4 #> 1985 697.1 1187.5 #> 1986 700.9 1367.9 #> 1987 743.2 1460.1 #> 1988 870.3 1570.0 #> 1989 923.8 1569.4 #> 1990 931.5 1563.1 #> 1991 944.7 1600.3 #> 1992 968.6 1650.9 #> 1993 970.0 1710.5 #> 1994 1000.4 1817.5 #> 1995 1067.4 1857.2 #> 1996 1081.6 1837.6 #> 1997 1111.3 1879.1 #> 1998 1137.7 1975.7 #> 1999 1207.2 2069.6 #> 2000 1232.5 2177.5 #> 2001 1344.8 2269.5 #> 2002 1495.1 2338.6 #> 2003 1528.2 2424.2 #> 2004 1536.7 2500.8 #> 2005 1540.4 2536.0 #> 2006 1623.3 2611.1 #> 2007 1687.9 2747.0 #> 2008 1675.9 2886.1 #> 2009 1736.6 2795.1 #> 2010 1705.5 2752.4 #> 2011 1660.0 2730.5 #> 2012 1672.9 2753.3 #> 2013 1668.9 2725.5 #> 2014 1687.7 2756.9 #> 2015 1730.5 2913.6 #> 2016 1684.3 2850.4 #> 2017 #> #> $model$variables #> [1] \"AO.220\" \"AO.219\" \"AO.277\" \"LS.400\" \"LS.280\" #> #> $model$X #> [,1] [,2] [,3] [,4] [,5] #> [1,] 0 0 0 -1 -1 #> [2,] 0 0 0 -1 -1 #> [3,] 0 0 0 -1 -1 #> [4,] 0 0 0 -1 -1 #> [5,] 0 0 0 -1 -1 #> [6,] 0 0 0 -1 -1 #> [7,] 0 0 0 -1 -1 #> [8,] 0 0 0 -1 -1 #> [9,] 0 0 0 -1 -1 #> [10,] 0 0 0 -1 -1 #> [11,] 0 0 0 -1 -1 #> [12,] 0 0 0 -1 -1 #> [13,] 0 0 0 -1 -1 #> [14,] 0 0 0 -1 -1 #> [15,] 0 0 0 -1 -1 #> [16,] 0 0 0 -1 -1 #> [17,] 0 0 0 -1 -1 #> [18,] 0 0 0 -1 -1 #> [19,] 0 0 0 -1 -1 #> [20,] 0 0 0 -1 -1 #> [21,] 0 0 0 -1 -1 #> [22,] 0 0 0 -1 -1 #> [23,] 0 0 0 -1 -1 #> [24,] 0 0 0 -1 -1 #> [25,] 0 0 0 -1 -1 #> [26,] 0 0 0 -1 -1 #> [27,] 0 0 0 -1 -1 #> [28,] 0 0 0 -1 -1 #> [29,] 0 0 0 -1 -1 #> [30,] 0 0 0 -1 -1 #> [31,] 0 0 0 -1 -1 #> [32,] 0 0 0 -1 -1 #> [33,] 0 0 0 -1 -1 #> [34,] 0 0 0 -1 -1 #> [35,] 0 0 0 -1 -1 #> [36,] 0 0 0 -1 -1 #> [37,] 0 0 0 -1 -1 #> [38,] 0 0 0 -1 -1 #> [39,] 0 0 0 -1 -1 #> [40,] 0 0 0 -1 -1 #> [41,] 0 0 0 -1 -1 #> [42,] 0 0 0 -1 -1 #> [43,] 0 0 0 -1 -1 #> [44,] 0 0 0 -1 -1 #> [45,] 0 0 0 -1 -1 #> [46,] 0 0 0 -1 -1 #> [47,] 0 0 0 -1 -1 #> [48,] 0 0 0 -1 -1 #> [49,] 0 0 0 -1 -1 #> [50,] 0 0 0 -1 -1 #> [51,] 0 0 0 -1 -1 #> [52,] 0 0 0 -1 -1 #> [53,] 0 0 0 -1 -1 #> [54,] 0 0 0 -1 -1 #> [55,] 0 0 0 -1 -1 #> [56,] 0 0 0 -1 -1 #> [57,] 0 0 0 -1 -1 #> [58,] 0 0 0 -1 -1 #> [59,] 0 0 0 -1 -1 #> [60,] 0 0 0 -1 -1 #> [61,] 0 0 0 -1 -1 #> [62,] 0 0 0 -1 -1 #> [63,] 0 0 0 -1 -1 #> [64,] 0 0 0 -1 -1 #> [65,] 0 0 0 -1 -1 #> [66,] 0 0 0 -1 -1 #> [67,] 0 0 0 -1 -1 #> [68,] 0 0 0 -1 -1 #> [69,] 0 0 0 -1 -1 #> [70,] 0 0 0 -1 -1 #> [71,] 0 0 0 -1 -1 #> [72,] 0 0 0 -1 -1 #> [73,] 0 0 0 -1 -1 #> [74,] 0 0 0 -1 -1 #> [75,] 0 0 0 -1 -1 #> [76,] 0 0 0 -1 -1 #> [77,] 0 0 0 -1 -1 #> [78,] 0 0 0 -1 -1 #> [79,] 0 0 0 -1 -1 #> [80,] 0 0 0 -1 -1 #> [81,] 0 0 0 -1 -1 #> [82,] 0 0 0 -1 -1 #> [83,] 0 0 0 -1 -1 #> [84,] 0 0 0 -1 -1 #> [85,] 0 0 0 -1 -1 #> [86,] 0 0 0 -1 -1 #> [87,] 0 0 0 -1 -1 #> [88,] 0 0 0 -1 -1 #> [89,] 0 0 0 -1 -1 #> [90,] 0 0 0 -1 -1 #> [91,] 0 0 0 -1 -1 #> [92,] 0 0 0 -1 -1 #> [93,] 0 0 0 -1 -1 #> [94,] 0 0 0 -1 -1 #> [95,] 0 0 0 -1 -1 #> [96,] 0 0 0 -1 -1 #> [97,] 0 0 0 -1 -1 #> [98,] 0 0 0 -1 -1 #> [99,] 0 0 0 -1 -1 #> [100,] 0 0 0 -1 -1 #> [101,] 0 0 0 -1 -1 #> [102,] 0 0 0 -1 -1 #> [103,] 0 0 0 -1 -1 #> [104,] 0 0 0 -1 -1 #> [105,] 0 0 0 -1 -1 #> [106,] 0 0 0 -1 -1 #> [107,] 0 0 0 -1 -1 #> [108,] 0 0 0 -1 -1 #> [109,] 0 0 0 -1 -1 #> [110,] 0 0 0 -1 -1 #> [111,] 0 0 0 -1 -1 #> [112,] 0 0 0 -1 -1 #> [113,] 0 0 0 -1 -1 #> [114,] 0 0 0 -1 -1 #> [115,] 0 0 0 -1 -1 #> [116,] 0 0 0 -1 -1 #> [117,] 0 0 0 -1 -1 #> [118,] 0 0 0 -1 -1 #> [119,] 0 0 0 -1 -1 #> [120,] 0 0 0 -1 -1 #> [121,] 0 0 0 -1 -1 #> [122,] 0 0 0 -1 -1 #> [123,] 0 0 0 -1 -1 #> [124,] 0 0 0 -1 -1 #> [125,] 0 0 0 -1 -1 #> [126,] 0 0 0 -1 -1 #> [127,] 0 0 0 -1 -1 #> [128,] 0 0 0 -1 -1 #> [129,] 0 0 0 -1 -1 #> [130,] 0 0 0 -1 -1 #> [131,] 0 0 0 -1 -1 #> [132,] 0 0 0 -1 -1 #> [133,] 0 0 0 -1 -1 #> [134,] 0 0 0 -1 -1 #> [135,] 0 0 0 -1 -1 #> [136,] 0 0 0 -1 -1 #> [137,] 0 0 0 -1 -1 #> [138,] 0 0 0 -1 -1 #> [139,] 0 0 0 -1 -1 #> [140,] 0 0 0 -1 -1 #> [141,] 0 0 0 -1 -1 #> [142,] 0 0 0 -1 -1 #> [143,] 0 0 0 -1 -1 #> [144,] 0 0 0 -1 -1 #> [145,] 0 0 0 -1 -1 #> [146,] 0 0 0 -1 -1 #> [147,] 0 0 0 -1 -1 #> [148,] 0 0 0 -1 -1 #> [149,] 0 0 0 -1 -1 #> [150,] 0 0 0 -1 -1 #> [151,] 0 0 0 -1 -1 #> [152,] 0 0 0 -1 -1 #> [153,] 0 0 0 -1 -1 #> [154,] 0 0 0 -1 -1 #> [155,] 0 0 0 -1 -1 #> [156,] 0 0 0 -1 -1 #> [157,] 0 0 0 -1 -1 #> [158,] 0 0 0 -1 -1 #> [159,] 0 0 0 -1 -1 #> [160,] 0 0 0 -1 -1 #> [161,] 0 0 0 -1 -1 #> [162,] 0 0 0 -1 -1 #> [163,] 0 0 0 -1 -1 #> [164,] 0 0 0 -1 -1 #> [165,] 0 0 0 -1 -1 #> [166,] 0 0 0 -1 -1 #> [167,] 0 0 0 -1 -1 #> [168,] 0 0 0 -1 -1 #> [169,] 0 0 0 -1 -1 #> [170,] 0 0 0 -1 -1 #> [171,] 0 0 0 -1 -1 #> [172,] 0 0 0 -1 -1 #> [173,] 0 0 0 -1 -1 #> [174,] 0 0 0 -1 -1 #> [175,] 0 0 0 -1 -1 #> [176,] 0 0 0 -1 -1 #> [177,] 0 0 0 -1 -1 #> [178,] 0 0 0 -1 -1 #> [179,] 0 0 0 -1 -1 #> [180,] 0 0 0 -1 -1 #> [181,] 0 0 0 -1 -1 #> [182,] 0 0 0 -1 -1 #> [183,] 0 0 0 -1 -1 #> [184,] 0 0 0 -1 -1 #> [185,] 0 0 0 -1 -1 #> [186,] 0 0 0 -1 -1 #> [187,] 0 0 0 -1 -1 #> [188,] 0 0 0 -1 -1 #> [189,] 0 0 0 -1 -1 #> [190,] 0 0 0 -1 -1 #> [191,] 0 0 0 -1 -1 #> [192,] 0 0 0 -1 -1 #> [193,] 0 0 0 -1 -1 #> [194,] 0 0 0 -1 -1 #> [195,] 0 0 0 -1 -1 #> [196,] 0 0 0 -1 -1 #> [197,] 0 0 0 -1 -1 #> [198,] 0 0 0 -1 -1 #> [199,] 0 0 0 -1 -1 #> [200,] 0 0 0 -1 -1 #> [201,] 0 0 0 -1 -1 #> [202,] 0 0 0 -1 -1 #> [203,] 0 0 0 -1 -1 #> [204,] 0 0 0 -1 -1 #> [205,] 0 0 0 -1 -1 #> [206,] 0 0 0 -1 -1 #> [207,] 0 0 0 -1 -1 #> [208,] 0 0 0 -1 -1 #> [209,] 0 0 0 -1 -1 #> [210,] 0 0 0 -1 -1 #> [211,] 0 0 0 -1 -1 #> [212,] 0 0 0 -1 -1 #> [213,] 0 0 0 -1 -1 #> [214,] 0 0 0 -1 -1 #> [215,] 0 0 0 -1 -1 #> [216,] 0 0 0 -1 -1 #> [217,] 0 0 0 -1 -1 #> [218,] 0 0 0 -1 -1 #> [219,] 0 1 0 -1 -1 #> [220,] 1 0 0 -1 -1 #> [221,] 0 0 0 -1 -1 #> [222,] 0 0 0 -1 -1 #> [223,] 0 0 0 -1 -1 #> [224,] 0 0 0 -1 -1 #> [225,] 0 0 0 -1 -1 #> [226,] 0 0 0 -1 -1 #> [227,] 0 0 0 -1 -1 #> [228,] 0 0 0 -1 -1 #> [229,] 0 0 0 -1 -1 #> [230,] 0 0 0 -1 -1 #> [231,] 0 0 0 -1 -1 #> [232,] 0 0 0 -1 -1 #> [233,] 0 0 0 -1 -1 #> [234,] 0 0 0 -1 -1 #> [235,] 0 0 0 -1 -1 #> [236,] 0 0 0 -1 -1 #> [237,] 0 0 0 -1 -1 #> [238,] 0 0 0 -1 -1 #> [239,] 0 0 0 -1 -1 #> [240,] 0 0 0 -1 -1 #> [241,] 0 0 0 -1 -1 #> [242,] 0 0 0 -1 -1 #> [243,] 0 0 0 -1 -1 #> [244,] 0 0 0 -1 -1 #> [245,] 0 0 0 -1 -1 #> [246,] 0 0 0 -1 -1 #> [247,] 0 0 0 -1 -1 #> [248,] 0 0 0 -1 -1 #> [249,] 0 0 0 -1 -1 #> [250,] 0 0 0 -1 -1 #> [251,] 0 0 0 -1 -1 #> [252,] 0 0 0 -1 -1 #> [253,] 0 0 0 -1 -1 #> [254,] 0 0 0 -1 -1 #> [255,] 0 0 0 -1 -1 #> [256,] 0 0 0 -1 -1 #> [257,] 0 0 0 -1 -1 #> [258,] 0 0 0 -1 -1 #> [259,] 0 0 0 -1 -1 #> [260,] 0 0 0 -1 -1 #> [261,] 0 0 0 -1 -1 #> [262,] 0 0 0 -1 -1 #> [263,] 0 0 0 -1 -1 #> [264,] 0 0 0 -1 -1 #> [265,] 0 0 0 -1 -1 #> [266,] 0 0 0 -1 -1 #> [267,] 0 0 0 -1 -1 #> [268,] 0 0 0 -1 -1 #> [269,] 0 0 0 -1 -1 #> [270,] 0 0 0 -1 -1 #> [271,] 0 0 0 -1 -1 #> [272,] 0 0 0 -1 -1 #> [273,] 0 0 0 -1 -1 #> [274,] 0 0 0 -1 -1 #> [275,] 0 0 0 -1 -1 #> [276,] 0 0 0 -1 -1 #> [277,] 0 0 1 -1 -1 #> [278,] 0 0 0 -1 -1 #> [279,] 0 0 0 -1 -1 #> [280,] 0 0 0 -1 0 #> [281,] 0 0 0 -1 0 #> [282,] 0 0 0 -1 0 #> [283,] 0 0 0 -1 0 #> [284,] 0 0 0 -1 0 #> [285,] 0 0 0 -1 0 #> [286,] 0 0 0 -1 0 #> [287,] 0 0 0 -1 0 #> [288,] 0 0 0 -1 0 #> [289,] 0 0 0 -1 0 #> [290,] 0 0 0 -1 0 #> [291,] 0 0 0 -1 0 #> [292,] 0 0 0 -1 0 #> [293,] 0 0 0 -1 0 #> [294,] 0 0 0 -1 0 #> [295,] 0 0 0 -1 0 #> [296,] 0 0 0 -1 0 #> [297,] 0 0 0 -1 0 #> [298,] 0 0 0 -1 0 #> [299,] 0 0 0 -1 0 #> [300,] 0 0 0 -1 0 #> [301,] 0 0 0 -1 0 #> [302,] 0 0 0 -1 0 #> [303,] 0 0 0 -1 0 #> [304,] 0 0 0 -1 0 #> [305,] 0 0 0 -1 0 #> [306,] 0 0 0 -1 0 #> [307,] 0 0 0 -1 0 #> [308,] 0 0 0 -1 0 #> [309,] 0 0 0 -1 0 #> [310,] 0 0 0 -1 0 #> [311,] 0 0 0 -1 0 #> [312,] 0 0 0 -1 0 #> [313,] 0 0 0 -1 0 #> [314,] 0 0 0 -1 0 #> [315,] 0 0 0 -1 0 #> [316,] 0 0 0 -1 0 #> [317,] 0 0 0 -1 0 #> [318,] 0 0 0 -1 0 #> [319,] 0 0 0 -1 0 #> [320,] 0 0 0 -1 0 #> [321,] 0 0 0 -1 0 #> [322,] 0 0 0 -1 0 #> [323,] 0 0 0 -1 0 #> [324,] 0 0 0 -1 0 #> [325,] 0 0 0 -1 0 #> [326,] 0 0 0 -1 0 #> [327,] 0 0 0 -1 0 #> [328,] 0 0 0 -1 0 #> [329,] 0 0 0 -1 0 #> [330,] 0 0 0 -1 0 #> [331,] 0 0 0 -1 0 #> [332,] 0 0 0 -1 0 #> [333,] 0 0 0 -1 0 #> [334,] 0 0 0 -1 0 #> [335,] 0 0 0 -1 0 #> [336,] 0 0 0 -1 0 #> [337,] 0 0 0 -1 0 #> [338,] 0 0 0 -1 0 #> [339,] 0 0 0 -1 0 #> [340,] 0 0 0 -1 0 #> [341,] 0 0 0 -1 0 #> [342,] 0 0 0 -1 0 #> [343,] 0 0 0 -1 0 #> [344,] 0 0 0 -1 0 #> [345,] 0 0 0 -1 0 #> [346,] 0 0 0 -1 0 #> [347,] 0 0 0 -1 0 #> [348,] 0 0 0 -1 0 #> [349,] 0 0 0 -1 0 #> [350,] 0 0 0 -1 0 #> [351,] 0 0 0 -1 0 #> [352,] 0 0 0 -1 0 #> [353,] 0 0 0 -1 0 #> [354,] 0 0 0 -1 0 #> [355,] 0 0 0 -1 0 #> [356,] 0 0 0 -1 0 #> [357,] 0 0 0 -1 0 #> [358,] 0 0 0 -1 0 #> [359,] 0 0 0 -1 0 #> [360,] 0 0 0 -1 0 #> [361,] 0 0 0 -1 0 #> [362,] 0 0 0 -1 0 #> [363,] 0 0 0 -1 0 #> [364,] 0 0 0 -1 0 #> [365,] 0 0 0 -1 0 #> [366,] 0 0 0 -1 0 #> [367,] 0 0 0 -1 0 #> [368,] 0 0 0 -1 0 #> [369,] 0 0 0 -1 0 #> [370,] 0 0 0 -1 0 #> [371,] 0 0 0 -1 0 #> [372,] 0 0 0 -1 0 #> [373,] 0 0 0 -1 0 #> [374,] 0 0 0 -1 0 #> [375,] 0 0 0 -1 0 #> [376,] 0 0 0 -1 0 #> [377,] 0 0 0 -1 0 #> [378,] 0 0 0 -1 0 #> [379,] 0 0 0 -1 0 #> [380,] 0 0 0 -1 0 #> [381,] 0 0 0 -1 0 #> [382,] 0 0 0 -1 0 #> [383,] 0 0 0 -1 0 #> [384,] 0 0 0 -1 0 #> [385,] 0 0 0 -1 0 #> [386,] 0 0 0 -1 0 #> [387,] 0 0 0 -1 0 #> [388,] 0 0 0 -1 0 #> [389,] 0 0 0 -1 0 #> [390,] 0 0 0 -1 0 #> [391,] 0 0 0 -1 0 #> [392,] 0 0 0 -1 0 #> [393,] 0 0 0 -1 0 #> [394,] 0 0 0 -1 0 #> [395,] 0 0 0 -1 0 #> [396,] 0 0 0 -1 0 #> [397,] 0 0 0 -1 0 #> [398,] 0 0 0 -1 0 #> [399,] 0 0 0 -1 0 #> [400,] 0 0 0 0 0 #> [401,] 0 0 0 0 0 #> [402,] 0 0 0 0 0 #> [403,] 0 0 0 0 0 #> [404,] 0 0 0 0 0 #> [405,] 0 0 0 0 0 #> [406,] 0 0 0 0 0 #> [407,] 0 0 0 0 0 #> [408,] 0 0 0 0 0 #> [409,] 0 0 0 0 0 #> [410,] 0 0 0 0 0 #> [411,] 0 0 0 0 0 #> [412,] 0 0 0 0 0 #> [413,] 0 0 0 0 0 #> [414,] 0 0 0 0 0 #> [415,] 0 0 0 0 0 #> [416,] 0 0 0 0 0 #> [417,] 0 0 0 0 0 #> [418,] 0 0 0 0 0 #> [419,] 0 0 0 0 0 #> [420,] 0 0 0 0 0 #> [421,] 0 0 0 0 0 #> [422,] 0 0 0 0 0 #> [423,] 0 0 0 0 0 #> [424,] 0 0 0 0 0 #> [425,] 0 0 0 0 0 #> #> $model$b #> [1] -210.91745 199.66569 -192.39785 84.32270 -77.78577 #> #> $model$bcov #> [,1] [,2] [,3] [,4] [,5] #> [1,] 1.258544e+03 5.076022e+01 -8.906666e-01 -1.197400e-05 -0.827996523 #> [2,] 5.076022e+01 1.258545e+03 -9.422003e-01 -1.454505e-05 -1.048251277 #> [3,] -8.906666e-01 -9.422003e-01 1.322416e+03 5.431398e-04 155.010519468 #> [4,] -1.197400e-05 -1.454505e-05 5.431398e-04 3.654102e+02 0.004446791 #> [5,] -8.279965e-01 -1.048251e+00 1.550105e+02 4.446791e-03 364.501084888 #> #> $model$linearized #> [1] 466.6369 509.1369 450.3369 465.6369 444.9369 471.6369 459.2369 #> [8] 529.4369 895.8369 385.7369 384.5369 478.6369 509.9369 517.1369 #> [15] 468.9369 474.8369 464.7369 489.2369 491.8369 575.2369 970.2369 #> [22] 421.2369 421.0369 491.2369 493.8369 604.4369 506.9369 549.9369 #> [29] 509.9369 529.3369 563.1369 629.7369 1045.9369 522.8369 459.0369 #> [36] 532.3369 594.2369 706.8369 568.3369 609.3369 589.0369 569.6369 #> [43] 643.6369 703.6369 1194.0369 577.0369 484.7369 553.9369 600.8369 #> [50] 758.1369 559.9369 669.7369 587.6369 668.4369 672.1369 707.4369 #> [57] 1374.4369 620.4369 519.7369 606.4369 680.6369 720.5369 677.0369 #> [64] 727.4369 608.1369 678.8369 715.6369 749.7369 1466.6369 637.5369 #> [71] 557.6369 684.6369 722.2369 747.3369 728.5369 690.0369 657.4369 #> [78] 729.8369 736.1369 876.8369 1576.5369 638.0369 558.5369 725.5369 #> [85] 704.1369 771.3369 792.8369 721.6369 730.3369 764.4369 758.2369 #> [92] 930.3369 1575.9369 684.7369 592.7369 733.3369 750.6369 822.0369 #> [99] 838.9369 716.8369 765.9369 747.6369 793.1369 938.0369 1569.6369 #> [106] 700.5369 611.2369 725.7369 754.7369 834.7369 753.4369 801.0369 #> [113] 776.9369 748.0369 865.1369 951.2369 1606.8369 746.5369 672.4369 #> [120] 708.0369 837.9369 885.1369 832.5369 794.7369 730.1369 826.3369 #> [127] 909.0369 975.1369 1657.4369 768.6369 649.5369 760.6369 847.2369 #> [134] 913.1369 893.6369 778.0369 735.2369 851.2369 893.4369 976.5369 #> [141] 1717.0369 752.2369 670.9369 828.0369 838.2369 914.5369 919.1369 #> [148] 789.4369 805.3369 893.5369 941.1369 1006.9369 1824.0369 758.9369 #> [155] 689.0369 817.7369 912.5369 933.7369 913.3369 887.1369 880.4369 #> [162] 863.3369 927.1369 1073.9369 1863.7369 839.6369 743.6369 818.5369 #> [169] 901.7369 969.3369 915.1369 914.5369 895.4369 840.2369 940.2369 #> [176] 1088.1369 1844.1369 847.4369 733.9369 864.4369 855.5369 1001.3369 #> [183] 836.7369 977.6369 842.5369 945.6369 983.4369 1117.8369 1885.6369 #> [190] 923.8369 722.7369 829.4369 976.6369 976.7369 855.9369 1048.8369 #> [197] 876.4369 945.9369 1027.8369 1144.2369 1982.2369 948.5369 744.9369 #> [204] 909.7369 959.7369 1017.7369 900.9369 1061.0369 906.0369 1008.8369 #> [211] 1050.2369 1213.7369 2076.1369 931.4369 804.7369 908.4369 1031.2369 #> [218] 1058.8369 972.3712 1076.7544 1015.7369 1061.1369 1076.9369 1239.0369 #> [225] 2184.0369 978.4369 821.1369 1024.0369 1045.7369 1130.0369 1031.4369 #> [232] 1107.3369 969.5369 1019.4369 1138.5369 1351.3369 2276.0369 1034.4369 #> [239] 847.9369 1050.4369 1081.8369 1197.4369 1149.5369 1082.2369 1072.4369 #> [246] 1066.6369 1217.9369 1501.6369 2345.1369 1105.8369 907.0369 1099.2369 #> [253] 1228.9369 1243.6369 1244.4369 1188.5369 1107.7369 1204.7369 1322.6369 #> [260] 1534.7369 2430.7369 1189.4369 996.3369 1137.9369 1283.6369 1286.8369 #> [267] 1390.6369 1312.4369 1173.3369 1324.4369 1364.8369 1543.2369 2507.3369 #> [274] 1252.8369 1043.8369 1307.3369 1352.6348 1270.7369 1460.7369 1374.4227 #> [281] 1295.0227 1362.1227 1398.7227 1624.7227 2620.3227 1278.0227 1122.0227 #> [288] 1288.8227 1432.9227 1351.9227 1513.3227 1496.3227 1323.5227 1303.4227 #> [295] 1428.9227 1707.6227 2695.4227 1351.6227 1131.3227 1415.9227 1386.9227 #> [302] 1449.4227 1575.8227 1546.6227 1399.8227 1437.6227 1524.9227 1772.2227 #> [309] 2831.3227 1482.1227 1224.8227 1436.0227 1480.9227 1505.4227 1485.9227 #> [316] 1666.6227 1352.7227 1467.6227 1536.7227 1760.2227 2970.4227 1535.3227 #> [323] 1140.9227 1471.2227 1593.4227 1603.7227 1584.8227 1655.0227 1425.8227 #> [330] 1484.2227 1618.6227 1820.9227 2879.4227 1553.4227 1196.2227 1464.2227 #> [337] 1474.0227 1511.5227 1635.7227 1665.3227 1408.3227 1506.3227 1549.2227 #> [344] 1789.8227 2836.7227 1496.9227 1201.8227 1405.9227 1556.9227 1493.2227 #> [351] 1556.2227 1616.8227 1377.8227 1430.0227 1489.0227 1744.3227 2814.8227 #> [358] 1446.7227 1216.0227 1433.5227 1475.5227 1541.2227 1700.7227 1507.7227 #> [365] 1443.3227 1452.1227 1526.9227 1757.2227 2837.6227 1481.7227 1197.9227 #> [372] 1481.6227 1423.4227 1526.2227 1621.7227 1474.9227 1421.5227 1443.7227 #> [379] 1547.6227 1753.2227 2809.8227 1535.3227 1149.2227 1377.5227 1527.2227 #> [386] 1496.1227 1545.9227 1585.9227 1338.5227 1440.7227 1563.0227 1772.0227 #> [393] 2841.2227 1555.5227 1138.1227 1451.5227 1526.5227 1513.0227 1565.2227 #> [400] 1540.9000 1331.9000 1400.1000 1566.3000 1730.5000 2913.6000 1519.2000 #> [407] 1155.8000 1451.5000 1451.0000 1449.7000 1596.1000 1468.3000 1293.9000 #> [414] 1393.5000 1497.4000 1684.3000 2850.4000 1428.5000 1092.4000 1370.3000 #> [421] 1522.6000 1452.4000 1557.2000 1445.5000 1303.1000 #> #> #> $likelihood #> $likelihood$initial #> $likelihood$initial$ll #> [1] -2218.964 #> #> $likelihood$initial$ssq #> [1] 1139531 #> #> $likelihood$initial$nobs #> [1] 425 #> #> $likelihood$initial$neffective #> [1] -1 #> #> $likelihood$initial$nparams #> [1] 3 #> #> $likelihood$initial$df #> [1] 409 #> #> $likelihood$initial$aic #> [1] 4443.928 #> #> $likelihood$initial$aicc #> [1] 4443.987 #> #> $likelihood$initial$bic #> [1] 4455.991 #> #> $likelihood$initial$bic2 #> [1] 10.81551 #> #> $likelihood$initial$bicc #> [1] 7.954332 #> #> $likelihood$initial$hannanquinn #> [1] 4448.7 #> #> #> $likelihood$final #> $likelihood$final$ll #> [1] -2167.948 #> #> $likelihood$final$ssq #> [1] 892303.8 #> #> $likelihood$final$nobs #> [1] 425 #> #> $likelihood$final$neffective #> [1] -1 #> #> $likelihood$final$nparams #> [1] 6 #> #> $likelihood$final$df #> [1] 406 #> #> $likelihood$final$aic #> [1] 4347.896 #> #> $likelihood$final$aicc #> [1] 4348.104 #> #> $likelihood$final$bic #> [1] 4372.023 #> #> $likelihood$final$bic2 #> [1] 10.61171 #> #> $likelihood$final$bicc #> [1] 7.753609 #> #> $likelihood$final$hannanquinn #> [1] 4357.439 #> #> #> #> attr(,\"class\") #> [1] \"JD3_REGARIMA_OUTLIERS\""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/userdefined_variables_x13.html","id":null,"dir":"Reference","previous_headings":"","what":"Display a list of all the available output objects — userdefined_variables_x13","title":"Display a list of all the available output objects — userdefined_variables_x13","text":"Function generating comprehensive list available output variables (series, parameters, diagnostics) estimation process x13(), regarima() x11() functions. items available default estimation output remainder can added using userdefined parameter. User-defined objects can retrieved list lists generated estimation process","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/userdefined_variables_x13.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display a list of all the available output objects — userdefined_variables_x13","text":"","code":"userdefined_variables_x13(x = c(\"X-13\", \"RegArima\", \"X-11\"))"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/userdefined_variables_x13.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display a list of all the available output objects — userdefined_variables_x13","text":"x character indicate estimation function output items list displayed.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/userdefined_variables_x13.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display a list of all the available output objects — userdefined_variables_x13","text":"vector containing names available output objects (series, diagnostics, parameters)","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/userdefined_variables_x13.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Display a list of all the available output objects — userdefined_variables_x13","text":"information examples related 'JDemetra+' features online documentation: https://jdemetra-new-documentation.netlify.app/","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/userdefined_variables_x13.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display a list of all the available output objects — userdefined_variables_x13","text":"","code":"userdefined_variables_x13(\"x13\") #> [1] \"adjust\" #> [2] \"arima.bd\" #> [3] \"arima.bp\" #> [4] \"arima.bphi(*)\" #> [5] \"arima.bq\" #> [6] \"arima.btheta(*)\" #> [7] \"arima.d\" #> [8] \"arima.p\" #> [9] \"arima.phi(*)\" #> [10] \"arima.q\" #> [11] \"arima.theta(*)\" #> [12] \"benchmarking.original\" #> [13] \"benchmarking.result\" #> [14] \"benchmarking.target\" #> [15] \"cal\" #> [16] \"cal_b\" #> [17] \"cal_b(?)\" #> [18] \"cal_f\" #> [19] \"cal_f(?)\" #> [20] \"decomposition.b1\" #> [21] \"decomposition.b10\" #> [22] \"decomposition.b11\" #> [23] \"decomposition.b13\" #> [24] \"decomposition.b17\" #> [25] \"decomposition.b2\" #> [26] \"decomposition.b20\" #> [27] \"decomposition.b3\" #> [28] \"decomposition.b4\" #> [29] \"decomposition.b5\" #> [30] \"decomposition.b6\" #> [31] \"decomposition.b7\" #> [32] \"decomposition.b8\" #> [33] \"decomposition.b9\" #> [34] \"decomposition.c1\" #> [35] \"decomposition.c10\" #> [36] \"decomposition.c11\" #> [37] \"decomposition.c13\" #> [38] \"decomposition.c17\" #> [39] \"decomposition.c2\" #> [40] \"decomposition.c20\" #> [41] \"decomposition.c4\" #> [42] \"decomposition.c5\" #> [43] \"decomposition.c6\" #> [44] \"decomposition.c7\" #> [45] \"decomposition.c9\" #> [46] \"decomposition.d1\" #> [47] \"decomposition.d10\" #> [48] \"decomposition.d11\" #> [49] \"decomposition.d12\" #> [50] \"decomposition.d13\" #> [51] \"decomposition.d2\" #> [52] \"decomposition.d4\" #> [53] \"decomposition.d5\" #> [54] \"decomposition.d6\" #> [55] \"decomposition.d7\" #> [56] \"decomposition.d8\" #> [57] \"decomposition.d9\" #> [58] \"decomposition.d9-global-msr\" #> [59] \"decomposition.d9-msr\" #> [60] \"decomposition.d9-msr-table\" #> [61] \"decomposition.i_cmp\" #> [62] \"decomposition.s_cmp\" #> [63] \"decomposition.s_cmp_b\" #> [64] \"decomposition.s_cmp_f\" #> [65] \"decomposition.sa_cmp\" #> [66] \"decomposition.sa_cmp_b\" #> [67] \"decomposition.sa_cmp_f\" #> [68] \"decomposition.seasonal-filters\" #> [69] \"decomposition.si_cmp\" #> [70] \"decomposition.t_cmp\" #> [71] \"decomposition.t_cmp_b\" #> [72] \"decomposition.t_cmp_f\" #> [73] \"decomposition.trend-filter\" #> [74] \"decomposition.x11-all\" #> [75] \"decomposition.y_cmp\" #> [76] \"decomposition.y_cmp_b\" #> [77] \"decomposition.y_cmp_f\" #> [78] \"det\" #> [79] \"det_b(?)\" #> [80] \"det_f(?)\" #> [81] \"det_i\" #> [82] \"det_i_b(?)\" #> [83] \"det_i_f(?)\" #> [84] \"det_s\" #> [85] \"det_s_b(?)\" #> [86] \"det_s_f(?)\" #> [87] \"det_t\" #> [88] \"det_t_b(?)\" #> [89] \"det_t_f(?)\" #> [90] \"diagnostics.fcast-insample-mean\" #> [91] \"diagnostics.fcast-outsample-mean\" #> [92] \"diagnostics.fcast-outsample-variance\" #> [93] \"diagnostics.seas-i-combined\" #> [94] \"diagnostics.seas-i-combined3\" #> [95] \"diagnostics.seas-i-evolutive\" #> [96] \"diagnostics.seas-i-f\" #> [97] \"diagnostics.seas-i-friedman\" #> [98] \"diagnostics.seas-i-kw\" #> [99] \"diagnostics.seas-i-periodogram\" #> [100] \"diagnostics.seas-i-qs\" #> [101] \"diagnostics.seas-i-spectralpeaks\" #> [102] \"diagnostics.seas-i-stable\" #> [103] \"diagnostics.seas-lin-combined\" #> [104] \"diagnostics.seas-lin-evolutive\" #> [105] \"diagnostics.seas-lin-f\" #> [106] \"diagnostics.seas-lin-friedman\" #> [107] \"diagnostics.seas-lin-kw\" #> [108] \"diagnostics.seas-lin-periodogram\" #> [109] \"diagnostics.seas-lin-qs\" #> [110] \"diagnostics.seas-lin-spectralpeaks\" #> [111] \"diagnostics.seas-lin-stable\" #> [112] \"diagnostics.seas-res-combined\" #> [113] \"diagnostics.seas-res-combined3\" #> [114] \"diagnostics.seas-res-evolutive\" #> [115] \"diagnostics.seas-res-f\" #> [116] \"diagnostics.seas-res-friedman\" #> [117] \"diagnostics.seas-res-kw\" #> [118] \"diagnostics.seas-res-periodogram\" #> [119] \"diagnostics.seas-res-qs\" #> [120] \"diagnostics.seas-res-spectralpeaks\" #> [121] \"diagnostics.seas-res-stable\" #> [122] \"diagnostics.seas-sa-ac1\" #> [123] \"diagnostics.seas-sa-combined\" #> [124] \"diagnostics.seas-sa-combined3\" #> [125] \"diagnostics.seas-sa-evolutive\" #> [126] \"diagnostics.seas-sa-f\" #> [127] \"diagnostics.seas-sa-friedman\" #> [128] \"diagnostics.seas-sa-kw\" #> [129] \"diagnostics.seas-sa-periodogram\" #> [130] \"diagnostics.seas-sa-qs\" #> [131] \"diagnostics.seas-sa-spectralpeaks\" #> [132] \"diagnostics.seas-sa-stable\" #> [133] \"diagnostics.seas-si-combined\" #> [134] \"diagnostics.seas-si-combined3\" #> [135] \"diagnostics.seas-si-evolutive\" #> [136] \"diagnostics.seas-si-stable\" #> [137] \"diagnostics.td-i-all\" #> [138] \"diagnostics.td-i-last\" #> [139] \"diagnostics.td-res-all\" #> [140] \"diagnostics.td-res-last\" #> [141] \"diagnostics.td-sa-all\" #> [142] \"diagnostics.td-sa-last\" #> [143] \"ee\" #> [144] \"ee_b(?)\" #> [145] \"ee_f(?)\" #> [146] \"finals.d11\" #> [147] \"finals.d11a\" #> [148] \"finals.d11b\" #> [149] \"finals.d12\" #> [150] \"finals.d12a\" #> [151] \"finals.d12b\" #> [152] \"finals.d13\" #> [153] \"finals.d16\" #> [154] \"finals.d16a\" #> [155] \"finals.d16b\" #> [156] \"finals.d18\" #> [157] \"finals.d18a\" #> [158] \"finals.d18b\" #> [159] \"finals.e1\" #> [160] \"finals.e11\" #> [161] \"finals.e2\" #> [162] \"finals.e3\" #> [163] \"i\" #> [164] \"l\" #> [165] \"likelihood.adjustedll\" #> [166] \"likelihood.aic\" #> [167] \"likelihood.aicc\" #> [168] \"likelihood.bic\" #> [169] \"likelihood.bic2\" #> [170] \"likelihood.bicc\" #> [171] \"likelihood.df\" #> [172] \"likelihood.hannanquinn\" #> [173] \"likelihood.ll\" #> [174] \"likelihood.neffectiveobs\" #> [175] \"likelihood.nobs\" #> [176] \"likelihood.nparams\" #> [177] \"likelihood.ssqerr\" #> [178] \"log\" #> [179] \"m-statistics.m1\" #> [180] \"m-statistics.m10\" #> [181] \"m-statistics.m11\" #> [182] \"m-statistics.m2\" #> [183] \"m-statistics.m3\" #> [184] \"m-statistics.m4\" #> [185] \"m-statistics.m5\" #> [186] \"m-statistics.m6\" #> [187] \"m-statistics.m7\" #> [188] \"m-statistics.m8\" #> [189] \"m-statistics.m9\" #> [190] \"m-statistics.q\" #> [191] \"m-statistics.q-m2\" #> [192] \"mhe\" #> [193] \"mhe_b(?)\" #> [194] \"mhe_f(?)\" #> [195] \"omhe\" #> [196] \"omhe_b(?)\" #> [197] \"omhe_f(?)\" #> [198] \"out\" #> [199] \"out_b(?)\" #> [200] \"out_f(?)\" #> [201] \"out_i\" #> [202] \"out_i_b(?)\" #> [203] \"out_i_f(?)\" #> [204] \"out_s\" #> [205] \"out_s_b(?)\" #> [206] \"out_s_f(?)\" #> [207] \"out_t\" #> [208] \"out_t_b(?)\" #> [209] \"out_t_f(?)\" #> [210] \"period\" #> [211] \"preadjustment.a1\" #> [212] \"preadjustment.a1a\" #> [213] \"preadjustment.a1b\" #> [214] \"preadjustment.a6\" #> [215] \"preadjustment.a7\" #> [216] \"preadjustment.a8\" #> [217] \"preadjustment.a8i\" #> [218] \"preadjustment.a8s\" #> [219] \"preadjustment.a8t\" #> [220] \"preadjustment.a9\" #> [221] \"preadjustment.a9sa\" #> [222] \"preadjustment.a9ser\" #> [223] \"preadjustment.a9u\" #> [224] \"reg_i\" #> [225] \"reg_i_b(?)\" #> [226] \"reg_i_f(?)\" #> [227] \"reg_s\" #> [228] \"reg_s_b(?)\" #> [229] \"reg_s_f(?)\" #> [230] \"reg_sa\" #> [231] \"reg_sa_b(?)\" #> [232] \"reg_sa_f(?)\" #> [233] \"reg_t\" #> [234] \"reg_t_b(?)\" #> [235] \"reg_t_f(?)\" #> [236] \"reg_u\" #> [237] \"reg_u_b(?)\" #> [238] \"reg_u_f(?)\" #> [239] \"reg_y\" #> [240] \"reg_y_b(?)\" #> [241] \"reg_y_f(?)\" #> [242] \"regression.description\" #> [243] \"regression.details.coefficients\" #> [244] \"regression.details.covar\" #> [245] \"regression.details.covar-ml\" #> [246] \"regression.easter\" #> [247] \"regression.espan.end\" #> [248] \"regression.espan.missing\" #> [249] \"regression.espan.n\" #> [250] \"regression.espan.start\" #> [251] \"regression.leaster\" #> [252] \"regression.lp\" #> [253] \"regression.mean\" #> [254] \"regression.missing(*)\" #> [255] \"regression.ml.parameters\" #> [256] \"regression.ml.pcorr\" #> [257] \"regression.ml.pcovar\" #> [258] \"regression.ml.pcovar-ml\" #> [259] \"regression.ml.pscore\" #> [260] \"regression.mu\" #> [261] \"regression.nao\" #> [262] \"regression.nlp\" #> [263] \"regression.nls\" #> [264] \"regression.nmh\" #> [265] \"regression.nout\" #> [266] \"regression.nso\" #> [267] \"regression.ntc\" #> [268] \"regression.ntd\" #> [269] \"regression.nusers\" #> [270] \"regression.out(*)\" #> [271] \"regression.outlier(*)\" #> [272] \"regression.td(*)\" #> [273] \"regression.type\" #> [274] \"regression.user(*)\" #> [275] \"residuals.bp\" #> [276] \"residuals.bp2\" #> [277] \"residuals.doornikhansen\" #> [278] \"residuals.kurtosis\" #> [279] \"residuals.lb\" #> [280] \"residuals.lb2\" #> [281] \"residuals.lruns\" #> [282] \"residuals.ludruns\" #> [283] \"residuals.mean\" #> [284] \"residuals.nruns\" #> [285] \"residuals.nudruns\" #> [286] \"residuals.res\" #> [287] \"residuals.seasbp\" #> [288] \"residuals.seaslb\" #> [289] \"residuals.ser\" #> [290] \"residuals.skewness\" #> [291] \"residuals.tsres\" #> [292] \"residuals.type\" #> [293] \"s\" #> [294] \"s_b\" #> [295] \"s_f\" #> [296] \"sa\" #> [297] \"sa_b\" #> [298] \"sa_f\" #> [299] \"span.end\" #> [300] \"span.missing\" #> [301] \"span.n\" #> [302] \"span.start\" #> [303] \"t\" #> [304] \"t_b\" #> [305] \"t_f\" #> [306] \"tde\" #> [307] \"tde_b(?)\" #> [308] \"tde_f(?)\" #> [309] \"variancedecomposition.cycle\" #> [310] \"variancedecomposition.irregular\" #> [311] \"variancedecomposition.others\" #> [312] \"variancedecomposition.seasonality\" #> [313] \"variancedecomposition.tdh\" #> [314] \"variancedecomposition.total\" #> [315] \"y\" #> [316] \"y_b\" #> [317] \"y_b(?)\" #> [318] \"y_eb(?)\" #> [319] \"y_ef(?)\" #> [320] \"y_f\" #> [321] \"y_f(?)\" #> [322] \"yc\" #> [323] \"ycal\" #> [324] \"ycal_f(?)\" userdefined_variables_x13(\"regarima\") #> [1] \"adjust\" \"arima.bd\" #> [3] \"arima.bp\" \"arima.bphi(*)\" #> [5] \"arima.bq\" \"arima.btheta(*)\" #> [7] \"arima.d\" \"arima.p\" #> [9] \"arima.phi(*)\" \"arima.q\" #> [11] \"arima.theta(*)\" \"cal\" #> [13] \"cal_b(?)\" \"cal_f(?)\" #> [15] \"det\" \"det_b(?)\" #> [17] \"det_f(?)\" \"det_i\" #> [19] \"det_i_b(?)\" \"det_i_f(?)\" #> [21] \"det_s\" \"det_s_b(?)\" #> [23] \"det_s_f(?)\" \"det_t\" #> [25] \"det_t_b(?)\" \"det_t_f(?)\" #> [27] \"ee\" \"ee_b(?)\" #> [29] \"ee_f(?)\" \"l\" #> [31] \"likelihood.adjustedll\" \"likelihood.aic\" #> [33] \"likelihood.aicc\" \"likelihood.bic\" #> [35] \"likelihood.bic2\" \"likelihood.bicc\" #> [37] \"likelihood.df\" \"likelihood.hannanquinn\" #> [39] \"likelihood.ll\" \"likelihood.neffectiveobs\" #> [41] \"likelihood.nobs\" \"likelihood.nparams\" #> [43] \"likelihood.ssqerr\" \"log\" #> [45] \"mhe\" \"mhe_b(?)\" #> [47] \"mhe_f(?)\" \"omhe\" #> [49] \"omhe_b(?)\" \"omhe_f(?)\" #> [51] \"out\" \"out_b(?)\" #> [53] \"out_f(?)\" \"out_i\" #> [55] \"out_i_b(?)\" \"out_i_f(?)\" #> [57] \"out_s\" \"out_s_b(?)\" #> [59] \"out_s_f(?)\" \"out_t\" #> [61] \"out_t_b(?)\" \"out_t_f(?)\" #> [63] \"period\" \"reg_i\" #> [65] \"reg_i_b(?)\" \"reg_i_f(?)\" #> [67] \"reg_s\" \"reg_s_b(?)\" #> [69] \"reg_s_f(?)\" \"reg_sa\" #> [71] \"reg_sa_b(?)\" \"reg_sa_f(?)\" #> [73] \"reg_t\" \"reg_t_b(?)\" #> [75] \"reg_t_f(?)\" \"reg_u\" #> [77] \"reg_u_b(?)\" \"reg_u_f(?)\" #> [79] \"reg_y\" \"reg_y_b(?)\" #> [81] \"reg_y_f(?)\" \"regression.description\" #> [83] \"regression.details.coefficients\" \"regression.details.covar\" #> [85] \"regression.details.covar-ml\" \"regression.easter\" #> [87] \"regression.espan.end\" \"regression.espan.missing\" #> [89] \"regression.espan.n\" \"regression.espan.start\" #> [91] \"regression.leaster\" \"regression.lp\" #> [93] \"regression.mean\" \"regression.missing(*)\" #> [95] \"regression.ml.parameters\" \"regression.ml.pcorr\" #> [97] \"regression.ml.pcovar\" \"regression.ml.pcovar-ml\" #> [99] \"regression.ml.pscore\" \"regression.mu\" #> [101] \"regression.nao\" \"regression.nlp\" #> [103] \"regression.nls\" \"regression.nmh\" #> [105] \"regression.nout\" \"regression.nso\" #> [107] \"regression.ntc\" \"regression.ntd\" #> [109] \"regression.nusers\" \"regression.out(*)\" #> [111] \"regression.outlier(*)\" \"regression.td(*)\" #> [113] \"regression.type\" \"regression.user(*)\" #> [115] \"residuals.bp\" \"residuals.bp2\" #> [117] \"residuals.doornikhansen\" \"residuals.kurtosis\" #> [119] \"residuals.lb\" \"residuals.lb2\" #> [121] \"residuals.lruns\" \"residuals.ludruns\" #> [123] \"residuals.mean\" \"residuals.nruns\" #> [125] \"residuals.nudruns\" \"residuals.res\" #> [127] \"residuals.seasbp\" \"residuals.seaslb\" #> [129] \"residuals.ser\" \"residuals.skewness\" #> [131] \"residuals.tsres\" \"residuals.type\" #> [133] \"span.end\" \"span.missing\" #> [135] \"span.n\" \"span.start\" #> [137] \"tde\" \"tde_b(?)\" #> [139] \"tde_f(?)\" \"y\" #> [141] \"y_b(?)\" \"y_eb(?)\" #> [143] \"y_ef(?)\" \"y_f(?)\" #> [145] \"yc\" \"ycal\" #> [147] \"ycal_f(?)\" userdefined_variables_x13(\"x11\") #> [1] \"b1\" \"b10\" \"b11\" \"b13\" #> [5] \"b17\" \"b2\" \"b20\" \"b3\" #> [9] \"b4\" \"b5\" \"b6\" \"b7\" #> [13] \"b8\" \"b9\" \"c1\" \"c10\" #> [17] \"c11\" \"c13\" \"c17\" \"c2\" #> [21] \"c20\" \"c4\" \"c5\" \"c6\" #> [25] \"c7\" \"c9\" \"d1\" \"d10\" #> [29] \"d11\" \"d12\" \"d13\" \"d2\" #> [33] \"d4\" \"d5\" \"d6\" \"d7\" #> [37] \"d8\" \"d9\" \"d9-global-msr\" \"d9-msr\" #> [41] \"d9-msr-table\" \"seasonal-filters\" \"trend-filter\" \"x11-all\""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11.html","id":null,"dir":"Reference","previous_headings":"","what":"X-11 Decomposition Algorithm — x11","title":"X-11 Decomposition Algorithm — x11","text":"X-11 Decomposition Algorithm","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"X-11 Decomposition Algorithm — x11","text":"","code":"x11(ts, spec = x11_spec(), userdefined = NULL)"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"X-11 Decomposition Algorithm — x11","text":"ts univariate time series. spec specification. userdefined vector containing additional output variables (see x13_dictionary()).","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"X-11 Decomposition Algorithm — x11","text":"","code":"y <- rjd3toolkit::ABS$X0.2.09.10.M x11_spec <- x11_spec() x11(y, x11_spec) #> Last values #> d1 d2 d4 d5 d6 d7 d8 d9 #> Mar 2017 1370.300 NA NA 0.8961795 1529.046 1549.437 0.8843860 NaN #> Apr 2017 1508.745 NA NA 0.9370285 1610.138 1551.362 0.9814599 0.9725289 #> May 2017 1452.400 NA NA 0.9240579 1571.763 1552.613 0.9354553 NaN #> Jun 2017 1557.200 NA NA 0.9906046 1571.969 1554.256 1.0018939 NaN #> Jul 2017 1451.753 NA NA 0.9669725 1501.339 1554.036 0.9301585 0.9341824 #> Aug 2017 1303.100 NA NA 0.8370380 1556.799 1554.112 0.8384851 NaN #> d10 d11 d12 d13 #> Mar 2017 0.8911974 1537.594 1549.121 0.9925591 #> Apr 2017 0.9472835 1607.333 1550.650 1.0365542 #> May 2017 0.9277056 1565.583 1551.579 1.0090258 #> Jun 2017 0.9945226 1565.776 1552.914 1.0082826 #> Jul 2017 0.9598855 1505.909 1552.756 0.9698294 #> Aug 2017 0.8381839 1554.671 1552.438 1.0014382 x11_spec <- set_x11(x11_spec, henderson.filter = 13) x11(y, x11_spec) #> Last values #> d1 d2 d4 d5 d6 d7 d8 d9 #> Mar 2017 1370.300 NA NA 0.8957968 1529.700 1556.611 0.8803097 NaN #> Apr 2017 1521.625 NA NA 0.9391175 1620.271 1562.636 0.9743791 0.9737554 #> May 2017 1452.400 NA NA 0.9239661 1571.919 1563.794 0.9287669 NaN #> Jun 2017 1557.200 NA NA 0.9916495 1570.313 1560.504 0.9978827 NaN #> Jul 2017 1453.790 NA NA 0.9669146 1503.535 1555.034 0.9295618 0.9348928 #> Aug 2017 1303.100 NA NA 0.8369658 1556.933 1547.539 0.8420469 NaN #> d10 d11 d12 d13 #> Mar 2017 0.8899565 1539.738 1556.256 0.9893865 #> Apr 2017 0.9498667 1602.962 1560.758 1.0270404 #> May 2017 0.9267578 1567.184 1560.959 1.0039883 #> Jun 2017 0.9952607 1564.615 1557.551 1.0045354 #> Jul 2017 0.9607704 1504.522 1552.689 0.9689783 #> Aug 2017 0.8393622 1552.488 1545.113 1.0047731"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Set X-11 Specification — set_x11","title":"Set X-11 Specification — set_x11","text":"Set X-11 Specification","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set X-11 Specification — set_x11","text":"","code":"set_x11( x, mode = c(NA, \"Undefined\", \"Additive\", \"Multiplicative\", \"LogAdditive\", \"PseudoAdditive\"), seasonal.comp = NA, seasonal.filter = NA, henderson.filter = NA, lsigma = NA, usigma = NA, fcasts = NA, bcasts = NA, calendar.sigma = c(NA, \"None\", \"Signif\", \"All\", \"Select\"), sigma.vector = NA, exclude.forecast = NA, bias = c(NA, \"LEGACY\") )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set X-11 Specification — set_x11","text":"x specification modified, object class \"JD3_X11_SPEC\", default X11 spec can obtained 'x=x11_spec()' mode character: decomposition mode. Determines mode seasonal adjustment decomposition performed: \"Undefined\" - assumption concerning relationship time series components made; \"Additive\" - assumes additive relationship; \"Multiplicative\" - assumes multiplicative relationship; \"LogAdditive\" - performs additive decomposition logarithms series adjusted; \"PseudoAdditive\" - assumes pseudo-additive relationship. changed program, needed. seasonal.comp logical: TRUE, program computes seasonal component. Otherwise, seasonal component estimated values set 0 (additive decomposition) 1 (multiplicative decomposition). seasonal.filter vector character(s) specifying seasonal moving average (.e. seasonal filter) used estimate seasonal factors entire series. vector can length: 1 - seasonal filter used periods (e.g.: seasonal.filter = \"Msr\" seasonal.filter = \"S3X3\" ); different value quarter (length 4) month (length 12) - (e.g. quarterly series: seasonal.filter = c(\"S3X3\", \"Msr\", \"S3X3\", \"Msr\")). Possible filters : \"Msr\", \"Stable\", \"X11Default\", \"S3X1\", \"S3X3\", \"S3X5\", \"S3X9\", \"S3X15\". \"Msr\" - program chooses final seasonal filter automatically. henderson.filter numeric: length Henderson filter (odd number 3 101). henderson.filter = 0 automatic selection Henderson filter's length trend estimation enabled. lsigma numeric: lower sigma boundary detection extreme values, > 0.5, default=1.5. usigma numeric: upper sigma boundary detection extreme values, > lsigma, default=2.5. bcasts, fcasts numeric: number backasts (bcasts) forecasts (fcasts) generated RegARIMA model periods (positive values) years (negative values).Default values: fcasts=-1 bcasts=0. calendar.sigma character specify standard errors used extreme values detection adjustment computed: 5 year spans irregulars (\"None\", default value); separately calendar period (\"\"); separately period Cochran's hypothesis test determines irregular component heteroskedastic calendar month/quarter (\"Signif\"); separately two complementary sets calendar months/quarters specified x11.sigmaVector parameter (\"Select\", see parameter sigma.vector). sigma.vector vector specify one two groups periods standard errors used extreme values detection adjustment computed separately. used calendar.sigma = \"Select\". Possible values : 1 2. exclude.forecast Boolean exclude forecasts backcasts. TRUE, RegARIMA model forecasts backcasts used detection extreme values seasonal adjustment routines.Default= FALSE. bias TODO.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set X-11 Specification — set_x11","text":"\"JD3_X11_SPEC\" object, containing parameters.","code":""},{"path":[]},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x11_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set X-11 Specification — set_x11","text":"","code":"init_spec <- x11_spec() new_spec <- set_x11(init_spec, mode = \"LogAdditive\", seasonal.comp = 1, seasonal.filter = \"S3X9\", henderson.filter = 7, lsigma = 1.7, usigma = 2.7, fcasts = -1, bcasts = -1, calendar.sigma =\"All\", sigma.vector = NA, exclude.forecast = FALSE, bias = \"LEGACY\")"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13.html","id":null,"dir":"Reference","previous_headings":"","what":"Seasonal Adjustment with X13-ARIMA — x13","title":"Seasonal Adjustment with X13-ARIMA — x13","text":"Seasonal Adjustment X13-ARIMA","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Seasonal Adjustment with X13-ARIMA — x13","text":"","code":"x13( ts, spec = c(\"rsa4\", \"rsa0\", \"rsa1\", \"rsa2c\", \"rsa3\", \"rsa5c\"), context = NULL, userdefined = NULL ) x13_fast( ts, spec = c(\"rsa4\", \"rsa0\", \"rsa1\", \"rsa2c\", \"rsa3\", \"rsa5c\"), context = NULL, userdefined = NULL ) jx13( ts, spec = c(\"rsa4\", \"rsa0\", \"rsa1\", \"rsa2c\", \"rsa3\", \"rsa5c\"), context = NULL, userdefined = NULL )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Seasonal Adjustment with X13-ARIMA — x13","text":"ts univariate time series. spec model specification. Can either name predefined specification user-defined specification. context list external regressors (calendar ) used estimation userdefined vector containing additional output variables (see x13_dictionary()).","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Seasonal Adjustment with X13-ARIMA — x13","text":"x13() function returns list results, estimation specification result specification, x13_fast() faster function returns results. jx13() functions returns results java object allow customize outputs packages (use rjd3toolkit::dictionary() get list variables rjd3toolkit::result() get specific variable). estimation functions x13() x13_fast() can directly use specification name (string). want customize specification create specification object first.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Seasonal Adjustment with X13-ARIMA — x13","text":"","code":"y = rjd3toolkit::ABS$X0.2.09.10.M x13_fast(y,\"rsa3\") #> RegARIMA #> Log-transformation: yes #> SARIMA model: (2,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> phi(1) 0.38042 0.06517 5.838 #> phi(2) 0.21398 0.06040 3.543 #> theta(1) -0.70246 0.05146 -13.650 #> btheta(1) -0.56259 0.06648 -8.462 #> #> Regression model: #> Estimate Std. Error T-stat #> TC (2000-06-01) 0.15476 0.03158 4.900 #> AO (2000-07-01) -0.29505 0.04425 -6.668 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 7 #> #> Loglikelihood: 720.2196 #> Adjusted loglikelihood: -2147.407 #> #> Standard error of the regression (ML estimate): 0.04181753 #> AIC: 4308.814 #> AICC: 4309.092 #> BIC: 4336.962 #> #> #> Decomposition #> Monitoring and Quality Assessment Statistics: #> M stats #> m1 0.095 #> m2 0.097 #> m3 2.384 #> m4 0.403 #> m5 1.940 #> m6 0.179 #> m7 0.053 #> m8 0.176 #> m9 0.047 #> m10 0.157 #> m11 0.154 #> q 0.560 #> qm2 0.617 #> #> Final filters: #> Seasonal filter: #> Trend filter: 23 terms Henderson moving average #> #> Diagnostics #> Relative contribution of the components to the stationary #> portion of the variance in the original series, #> after the removal of the long term trend (in %) #> #> Component #> cycle 13.259 #> seasonal 86.854 #> irregular 0.970 #> calendar 0.000 #> others 0.299 #> total 101.383 #> #> Residual seasonality tests #> P.value #> seas.ftest.i 0.994 #> seas.ftest.sa 0.999 #> seas.qstest.i 0.045 #> seas.qstest.sa 0.035 #> td.ftest.i 0.000 #> td.ftest.sa 0.000 #> #> #> Final #> Last values #> series sa trend seas irr #> Sep 2016 1393.5 1558.700 1547.466 0.8940140 1.0072601 #> Oct 2016 1497.4 1539.619 1543.359 0.9725785 0.9975766 #> Nov 2016 1684.3 1540.423 1541.287 1.0934011 0.9994390 #> Dec 2016 2850.4 1561.106 1541.060 1.8258848 1.0130078 #> Jan 2017 1428.5 1513.071 1542.061 0.9441065 0.9812005 #> Feb 2017 1092.4 1538.709 1543.697 0.7099456 0.9967689 #> Mar 2017 1370.3 1532.681 1545.484 0.8940545 0.9917157 #> Apr 2017 1522.6 1609.073 1547.232 0.9462594 1.0399684 #> May 2017 1452.4 1563.830 1548.782 0.9287456 1.0097158 #> Jun 2017 1557.2 1561.361 1549.791 0.9973351 1.0074658 #> Jul 2017 1445.5 1519.197 1550.206 0.9514898 0.9799965 #> Aug 2017 1303.1 1554.064 1550.306 0.8385112 1.0024242 x13(y,\"rsa5c\") #> RegARIMA #> Log-transformation: yes #> SARIMA model: (0,1,1) (1,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> theta(1) -0.81551 0.06932 -11.76 #> bphi(1) -0.43410 0.02650 -16.38 #> btheta(1) -0.82463 0.04458 -18.50 #> #> Regression model: #> Estimate Std. Error T-stat #> monday -0.009156 0.003278 -2.793 #> tuesday 0.004523 0.003316 1.364 #> wednesday -0.001181 0.003285 -0.360 #> thursday 0.013349 0.003304 4.040 #> friday -0.001501 0.003313 -0.453 #> saturday 0.014993 0.003293 4.553 #> easter 0.051061 0.006629 7.703 #> TC (2000-06-01) 0.162337 0.026546 6.115 #> AO (2000-07-01) -0.306371 0.032121 -9.538 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 13 #> #> Loglikelihood: 794.8103 #> Adjusted loglikelihood: -2072.816 #> #> Standard error of the regression (ML estimate): 0.03486636 #> AIC: 4171.633 #> AICC: 4172.548 #> BIC: 4223.906 #> #> #> Decomposition #> Monitoring and Quality Assessment Statistics: #> M stats #> m1 0.054 #> m2 0.049 #> m3 1.765 #> m4 0.089 #> m5 1.416 #> m6 0.012 #> m7 0.052 #> m8 0.160 #> m9 0.050 #> m10 0.124 #> m11 0.122 #> q 0.385 #> qm2 0.427 #> #> Final filters: #> Seasonal filter: #> Trend filter: 23 terms Henderson moving average #> #> Diagnostics #> Relative contribution of the components to the stationary #> portion of the variance in the original series, #> after the removal of the long term trend (in %) #> #> Component #> cycle 13.437 #> seasonal 86.536 #> irregular 0.489 #> calendar 0.599 #> others 0.323 #> total 101.383 #> #> Residual seasonality tests #> P.value #> seas.ftest.i 0.977 #> seas.ftest.sa 0.986 #> seas.qstest.i 0.010 #> seas.qstest.sa 0.004 #> td.ftest.i 0.993 #> td.ftest.sa 0.996 #> #> #> Final #> Last values #> series sa trend seas irr #> Sep 2016 1393.5 1544.956 1548.898 0.9019675 0.9974547 #> Oct 2016 1497.4 1569.404 1547.139 0.9541202 1.0143910 #> Nov 2016 1684.3 1523.294 1546.637 1.1056957 0.9849077 #> Dec 2016 2850.4 1536.881 1546.937 1.8546651 0.9934994 #> Jan 2017 1428.5 1547.497 1547.656 0.9231038 0.9998970 #> Feb 2017 1092.4 1545.588 1548.350 0.7067860 0.9982164 #> Mar 2017 1370.3 1556.927 1548.747 0.8801312 1.0052818 #> Apr 2017 1522.6 1587.924 1548.518 0.9588622 1.0254473 #> May 2017 1452.4 1555.232 1547.335 0.9338798 1.0051038 #> Jun 2017 1557.2 1539.880 1545.271 1.0112477 0.9965115 #> Jul 2017 1445.5 1532.110 1542.807 0.9434701 0.9930664 #> Aug 2017 1303.1 1524.546 1540.399 0.8547462 0.9897086 regarima_fast(y,\"rg0\") #> Log-transformation: no #> SARIMA model: (0,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> theta(1) -0.87640 0.02290 -38.275 #> btheta(1) -0.38755 0.05071 -7.642 #> #> No regression variables #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 3 #> #> Loglikelihood: -2218.964 #> Standard error of the regression (ML estimate): 52.59136 #> AIC: 4443.928 #> AICC: 4443.987 #> BIC: 4455.991 #> regarima(y,\"rg3\") #> Log-transformation: yes #> SARIMA model: (2,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> phi(1) 0.38042 0.06517 5.838 #> phi(2) 0.21398 0.06040 3.543 #> theta(1) -0.70246 0.05146 -13.650 #> btheta(1) -0.56259 0.06648 -8.462 #> #> Regression model: #> Estimate Std. Error T-stat #> TC (2000-06-01) 0.15476 0.03158 4.900 #> AO (2000-07-01) -0.29505 0.04425 -6.668 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 7 #> #> Loglikelihood: 720.2196 #> Adjusted loglikelihood: -2147.407 #> #> Standard error of the regression (ML estimate): 0.04181753 #> AIC: 4308.814 #> AICC: 4309.092 #> BIC: 4336.962 #> sp = x13_spec(\"rsa5c\") sp = rjd3toolkit::add_outlier(sp, type = c(\"AO\"), c(\"2015-01-01\", \"2010-01-01\")) sp = rjd3toolkit::set_transform( rjd3toolkit::set_tradingdays( rjd3toolkit::set_easter(sp, enabled = FALSE), option = \"workingdays\" ), fun = \"None\" ) x13(y,spec=sp) #> RegARIMA #> Log-transformation: no #> SARIMA model: (3,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> phi(1) 0.16557 0.06461 2.563 #> phi(2) 0.08780 0.06435 1.364 #> phi(3) -0.11287 0.06047 -1.867 #> theta(1) -0.86076 0.03927 -21.918 #> btheta(1) -0.22918 0.05193 -4.413 #> #> Regression model: #> Estimate Std. Error T-stat #> td 1.0015 0.8098 1.237 #> lp 29.6392 11.5469 2.567 #> AO (2010-01-01) 37.0476 34.4300 1.076 #> AO (2015-01-01) 27.9946 35.0234 0.799 #> AO (2000-06-01) 199.7536 34.5756 5.777 #> AO (2000-07-01) -194.6887 34.6240 -5.623 #> LS (2005-04-01) -82.3062 17.4210 -4.725 #> LS (2015-07-01) 81.3334 18.0924 4.495 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 14 #> #> Loglikelihood: -2159.902 #> Standard error of the regression (ML estimate): 45.632 #> AIC: 4347.804 #> AICC: 4348.862 #> BIC: 4404.099 #> #> #> Decomposition #> Monitoring and Quality Assessment Statistics: #> M stats #> m1 0.043 #> m2 0.033 #> m3 1.909 #> m4 0.447 #> m5 1.607 #> m6 0.399 #> m7 0.167 #> m8 0.298 #> m9 0.145 #> m10 0.308 #> m11 0.293 #> q 0.552 #> qm2 0.624 #> #> Final filters: #> Seasonal filter: #> Trend filter: 23 terms Henderson moving average #> #> Diagnostics #> Relative contribution of the components to the stationary #> portion of the variance in the original series, #> after the removal of the long term trend (in %) #> #> Component #> cycle 6.178 #> seasonal 94.402 #> irregular 0.325 #> calendar 0.020 #> others 1.504 #> total 102.429 #> #> Residual seasonality tests #> P.value #> seas.ftest.i 0.902 #> seas.ftest.sa 0.996 #> seas.qstest.i 0.038 #> seas.qstest.sa 0.138 #> td.ftest.i 0.000 #> td.ftest.sa 0.000 #> #> #> Final #> Last values #> series sa trend seas irr #> Sep 2016 1393.5 1567.439 1554.386 -173.938673 13.052790 #> Oct 2016 1497.4 1547.052 1551.116 -49.651713 -4.064205 #> Nov 2016 1684.3 1544.888 1549.070 139.411575 -4.182012 #> Dec 2016 2850.4 1560.255 1548.078 1290.145096 12.177219 #> Jan 2017 1428.5 1529.584 1547.770 -101.084412 -18.185622 #> Feb 2017 1092.4 1552.751 1547.915 -460.350943 4.835817 #> Mar 2017 1370.3 1532.112 1548.227 -161.812246 -16.114386 #> Apr 2017 1522.6 1571.429 1548.386 -48.829077 23.042779 #> May 2017 1452.4 1554.351 1548.282 -101.951014 6.069336 #> Jun 2017 1557.2 1548.139 1547.682 9.061423 0.456243 #> Jul 2017 1445.5 1540.321 1546.422 -94.820887 -6.101117 #> Aug 2017 1303.1 1554.506 1544.648 -251.405622 9.857735 sp = set_x11(sp, henderson.filter = 13) x13_fast(y, spec = sp) #> RegARIMA #> Log-transformation: no #> SARIMA model: (3,1,1) (0,1,1) #> #> Coefficients #> Estimate Std. Error T-stat #> phi(1) 0.16557 0.06461 2.563 #> phi(2) 0.08780 0.06435 1.364 #> phi(3) -0.11287 0.06047 -1.867 #> theta(1) -0.86076 0.03927 -21.918 #> btheta(1) -0.22918 0.05193 -4.413 #> #> Regression model: #> Estimate Std. Error T-stat #> td 1.0015 0.8098 1.237 #> lp 29.6392 11.5469 2.567 #> AO (2010-01-01) 37.0476 34.4300 1.076 #> AO (2015-01-01) 27.9946 35.0234 0.799 #> AO (2000-06-01) 199.7536 34.5756 5.777 #> AO (2000-07-01) -194.6887 34.6240 -5.623 #> LS (2005-04-01) -82.3062 17.4210 -4.725 #> LS (2015-07-01) 81.3334 18.0924 4.495 #> Number of observations: 425 #> Number of effective observations: 412 #> Number of parameters: 14 #> #> Loglikelihood: -2159.902 #> Standard error of the regression (ML estimate): 45.632 #> AIC: 4347.804 #> AICC: 4348.862 #> BIC: 4404.099 #> #> #> Decomposition #> Monitoring and Quality Assessment Statistics: #> M stats #> m1 0.036 #> m2 0.029 #> m3 1.786 #> m4 0.537 #> m5 1.528 #> m6 0.449 #> m7 0.167 #> m8 0.290 #> m9 0.144 #> m10 0.297 #> m11 0.285 #> q 0.533 #> qm2 0.604 #> #> Final filters: #> Seasonal filter: #> Trend filter: 13 terms Henderson moving average #> #> Diagnostics #> Relative contribution of the components to the stationary #> portion of the variance in the original series, #> after the removal of the long term trend (in %) #> #> Component #> cycle 6.227 #> seasonal 94.535 #> irregular 0.290 #> calendar 0.020 #> others 1.506 #> total 102.578 #> #> Residual seasonality tests #> P.value #> seas.ftest.i 0.924 #> seas.ftest.sa 0.997 #> seas.qstest.i 0.018 #> seas.qstest.sa 0.117 #> td.ftest.i 0.000 #> td.ftest.sa 0.000 #> #> #> Final #> Last values #> series sa trend seas irr #> Sep 2016 1393.5 1567.255 1554.069 -173.754955 13.186079 #> Oct 2016 1497.4 1547.824 1551.331 -50.424459 -3.506850 #> Nov 2016 1684.3 1546.128 1548.057 138.172189 -1.929197 #> Dec 2016 2850.4 1557.769 1545.941 1292.630528 11.828053 #> Jan 2017 1428.5 1530.142 1545.777 -101.642006 -15.634829 #> Feb 2017 1092.4 1552.860 1546.573 -460.459760 6.287131 #> Mar 2017 1370.3 1532.029 1548.853 -161.728778 -16.824581 #> Apr 2017 1522.6 1571.073 1551.472 -48.473184 19.601512 #> May 2017 1452.4 1554.849 1552.698 -102.448692 2.151163 #> Jun 2017 1557.2 1550.052 1552.154 7.148413 -2.102681 #> Jul 2017 1445.5 1541.002 1549.653 -95.502435 -8.650144 #> Aug 2017 1303.1 1554.802 1545.625 -251.702019 9.176569"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_dictionary.html","id":null,"dir":"Reference","previous_headings":"","what":"X-13 Dictionary — x13_dictionary","title":"X-13 Dictionary — x13_dictionary","text":"X-13 Dictionary","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_dictionary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"X-13 Dictionary — x13_dictionary","text":"","code":"x13_dictionary()"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_dictionary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"X-13 Dictionary — x13_dictionary","text":"vector containing names available output objects (series, diagnostics, parameters).","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_revisions.html","id":null,"dir":"Reference","previous_headings":"","what":"Revisions History — x13_revisions","title":"Revisions History — x13_revisions","text":"Compute revisions history","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_revisions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Revisions History — x13_revisions","text":"","code":"x13_revisions( ts, spec, data_ids = NULL, ts_ids = NULL, cmp_ids = NULL, context = NULL )"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_revisions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Revisions History — x13_revisions","text":"ts time series used estimation. spec specification used. data_ids list list specify statistics export. sub-list must contain two elements: start (first date compute history, format \"YYYY-MM-DD\") id (name statistics, see x13_dictionary()). See example. ts_ids list list specify specific date component whose history studied. sub-list must contain three elements: start (first date compute history, format \"YYYY-MM-DD\"), period (date studied) id (name component, see x13_dictionary()). See example. cmp_ids list list specify component whose history studied. sub-list must contain three elements: start (first date compute history, format \"YYYY-MM-DD\"), end (last date compute history, format \"YYYY-MM-DD\") id (name component, see x13_dictionary()). many series periods start end exported. See example. context context specification.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_revisions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Revisions History — x13_revisions","text":"","code":"s <- rjd3toolkit::ABS$X0.2.09.10.M sa_mod <- x13(s) data_ids <- list( # Get the coefficient of the trading-day coefficient from 2005-jan list(start = \"2005-01-01\", id = \"regression.td(1)\"), # Get the ljung-box statistics on residuals from 2010-jan list(start = \"2010-01-01\", id = \"residuals.lb\")) ts_ids <- list( # Get the SA component estimates of 2010-jan from 2010-jan list(period = \"2010-01-01\", start = \"2010-01-01\", id = \"sa\"), # Get the irregular component estimates of 2010-jan from 2015-jan list(period = \"2010-01-01\", start = \"2015-01-01\", id = \"i\")) cmp_ids <- list( # Get the SA component estimates (full time series) 2010-jan to 2020-jan list(start = \"2010-01-01\", end = \"2020-01-01\", id = \"sa\"), # Get the trend component estimates (full time series) 2010-jan to 2020-jan list(start = \"2010-01-01\", end = \"2020-01-01\", id = \"t\")) rh <- x13_revisions(s, sa_mod$result_spec, data_ids, ts_ids, cmp_ids)"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"RegARIMA/X-13 Default Specifications — x13_spec","title":"RegARIMA/X-13 Default Specifications — x13_spec","text":"Set functions create default specification objects associated X-13ARIMA seasonal adjustment method.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RegARIMA/X-13 Default Specifications — x13_spec","text":"","code":"regarima_spec(name = c(\"rg4\", \"rg0\", \"rg1\", \"rg2c\", \"rg3\", \"rg5c\")) x13_spec(name = c(\"rsa4\", \"rsa0\", \"rsa1\", \"rsa2c\", \"rsa3\", \"rsa5c\")) x11_spec()"},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RegARIMA/X-13 Default Specifications — x13_spec","text":"name name predefined specification.","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RegARIMA/X-13 Default Specifications — x13_spec","text":"object class \"JD3_X13_SPEC\" (x13_spec()), \"JD3_REGARIMA_SPEC\" (regarima_spec()) \"JD3_X11_SPEC\" (x11_spec()).","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_spec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RegARIMA/X-13 Default Specifications — x13_spec","text":"Specification setting sheer X-11 decomposition method (without reg-arima pre-adjustment) supported x11_spec() function appear among possible X13-Arima default specifications Specification setting can restricted reg-arima part regarima_spec() function, without argument regarima_spec() yields RG5c specification setting complete X13-Arima spec, x13_spec() without argument yields RSA5c specification available predefined 'JDemetra+' model specifications described table : #' @seealso set pre-processing parameters: rjd3toolkit::set_arima(), rjd3toolkit::set_automodel(), rjd3toolkit::set_basic(), rjd3toolkit::set_easter(), rjd3toolkit::set_estimate(), rjd3toolkit::set_outlier(), rjd3toolkit::set_tradingdays(), rjd3toolkit::set_transform(), rjd3toolkit::add_outlier(), rjd3toolkit::remove_outlier(), rjd3toolkit::add_ramp(), rjd3toolkit::remove_ramp(), rjd3toolkit::add_usrdefvar(). set decomposition parameters: set_x11(). set benchmarking parameters: rjd3toolkit::set_benchmarking().","code":""},{"path":"https://rjdemetra.github.io/rjd3x13/reference/x13_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RegARIMA/X-13 Default Specifications — x13_spec","text":"","code":"init_spec <- x11_spec() init_spec <- regarima_spec(\"rg4\") init_spec <- x13_spec(\"rsa5c\")"}]