Update README.md

README.md

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-# AutoregressiveBase.jl
-Essential functionality and namespace for autoregressive models
+# AutoregressiveModels.jl
+
+*Essential toolkits for working with autoregressive models*
+
+[![CI-stable][CI-stable-img]][CI-stable-url]
+[![codecov][codecov-img]][codecov-url]
+[![PkgEval][pkgeval-img]][pkgeval-url]
+
+[CI-stable-img]: https://github.com/junyuan-chen/AutoregressiveModels.jl/workflows/CI-stable/badge.svg
+[CI-stable-url]: https://github.com/junyuan-chen/AutoregressiveModels.jl/actions?query=workflow%3ACI-stable
+
+[codecov-img]: https://codecov.io/gh/junyuan-chen/AutoregressiveModels.jl/branch/main/graph/badge.svg
+[codecov-url]: https://codecov.io/gh/junyuan-chen/AutoregressiveModels.jl
+
+[pkgeval-img]: https://juliaci.github.io/NanosoldierReports/pkgeval_badges/A/AutoregressiveModels.svg
+[pkgeval-url]: https://juliaci.github.io/NanosoldierReports/pkgeval_badges/A/AutoregressiveModels.html
+
+[AutoregressiveModels.jl](https://github.com/junyuan-chen/AutoregressiveModels.jl)
+is a Julia package that provides essential toolkits for working with autoregressive models.
+Performance and reusability is prioritized over comprehensive coverage of functionalities,
+as a main goal of the package is to provide support
+for other packages with more specialized purposes.
+At this moment, the main focus is on vector autoregressions (VAR).
+Some basic support for the autoregressive-moving-average (ARMA) model is also included.
+
+## Example Usage
+
+To illustrate what the package offers,
+here is an example of estimating the impulse responses
+based on structural vector autoregressions (SVAR)
+and producing a simultaneous confidence band with bootstrap.
+Details for individual functions may be found
+from docstrings in the help mode of Julia REPL.
+
+### Impulse Responses from Structural VAR
+
+The example below reproduces one application from Montiel Olea and Plagborg-Møller (2019).
+The data used are from Gertler and Karadi (2015).
+
+#### Step 1: Model Specification and Point Estimates
+
+```julia
+using AutoregressiveModels, CSV, ConfidenceBands
+using LocalProjections: datafile # Only needed for the data file
+
+# Load a prepared data file from Gertler and Karadi (2015)
+data = CSV.File(datafile(:gk))
+# Specify the variables for VAR (the order matters)
+names = (:logcpi, :logip, :gs1, :ebp)
+# Estimate VAR(12) with OLS and conduct Cholesky factorization for identification
+r = fit(VARProcess, data, names, 12, choleskyresid=true, adjust_dofr=false)
+# Compute point estimates of impulse responses (37 horizons) to the structural shock (3)
+irf = impulse(r, 3, 37, choleskyshock=true)
+```
+
+#### Step 2: Bootstrap Confidence Band
+
+A flexible autoregressive bootstrap framework is defined via `bootstrap!`
+and can be used to produce the draws of estimates for
+`SuptQuantileBootBand()` implemented in
+[ConfidenceBands.jl](https://github.com/junyuan-chen/ConfidenceBands.jl):
+
+```julia
+# Define how the bootstrap statistics are computed
+# See the docstring of bootstrap! for explanations
+fillirf!(x) = impulse!(x.out, x.r, 3, choleskyshock=true)
+ndraw = 10000
+# Preallocate an output array for statistics computed over the bootstrap iterations
+bootirfs = Array{Float64, 3}(undef, 4, 37, ndraw)
+
+# Specify the bootstrap procedure
+bootstrap!(bootirfs=>fillirf!, r, initialindex=1, drawresid=iidresiddraw!)
+# Produce a confidence band from the result
+boot2 = view(bootirfs, 2, :, :)
+lb, ub, pwlevel = confint(SuptQuantileBootBand(), boot2, level=0.68)
+```
+
+#### Step 3: Visualization
+
+Here is a plot for the results with the complete script located
+[here](https://raw.githubusercontent.com/junyuan-chen/AutoregressiveModels.jl/main/docs/src/plots/readmeexample.jl):
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/junyuan-chen/AutoregressiveModels.jl/main/docs/src/assets/readmeexample.svg" height="252"><br>
+</p>
+
+## References
+
+**Gertler, Mark, and Peter Karadi.** 2015.
+"Replication Data for: Monetary Policy Surprises, Credit Costs, and Economic Activity."
+*American Economic Association* [publisher], Inter-university Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/E114082V1.
+
+**Montiel Olea, José Luis and Mikkel Plagborg-Møller.** 2019.
+"Simultaneous Confidence Bands: Theory, Implementation, and an Application to SVARs."
+*Journal of Applied Econometrics* 34 (1): 1-17.