remove unused import

jdebacker · Jun 14, 2024 · 7a54015 · 7a54015
1 parent ac1f068
commit 7a54015
Showing 1 changed file with 24 additions and 16 deletions.
diff --git a/ogzaf/macro_params.py b/ogzaf/macro_params.py
@@ -1,7 +1,7 @@
 """
-This module uses data from World Bank WDI, World Bank Quarterly Public Sector Debt (QPSD) database,
-UN Data Portal, and FRED to find values for parameters for the
-OG-ZAF model that rely on macro data for calibration.
+This module uses data from World Bank WDI, World Bank Quarterly Public
+Sector Debt (QPSD) database, UN Data Portal, and FRED to find values for
+parameters for the OG-ZAF model that rely on macro data for calibration.
 """
 
 # imports
@@ -12,7 +12,6 @@
 import requests
 import datetime
 import statsmodels.api as sm
-from ogzaf.utils import get_legacy_session
 from io import StringIO
 
 
@@ -39,6 +38,7 @@ def get_macro_params():
         "GDP per capita (constant 2015 US$)": "NY.GDP.PCAP.KD",
         "Real GDP (constant 2015 US$)": "NY.GDP.MKTP.KD",
         "Nominal GDP (current US$)": "NY.GDP.MKTP.CD",
+        "General government final consumption expenditure (current US$)": "NE.CON.GOVT.CD",
         # "General government debt (percentage of GDP)": "GC.DOD.TOTL.GD.ZS",
         # "GDP per capita (current US$)": "NY.GDP.PCAP.CD",
         # "GDP per person employed (constant 2017 PPP$)": "SL.GDP.PCAP.EM.KD",
@@ -119,18 +119,22 @@ def get_macro_params():
         # "Labor share": "LABSHPINA156NRUG",
         # "BAA Corp Bond Rates": "DBAA",
         # "10 year govt bond rate": "IRLTLT01ZAM156N",
+        # "Nominal GDP": "MKTGDPZAA646NWDB",
+        "Total Expenditure to GDP": "ZAFGGXG01GDPPT",
         "Total gov transfer payments": "B087RC1Q027SBEA",
         "Social Security payments": "W823RC1",
         "Gov interest payments": "A091RC1Q027SBEA",
         "Labor share of income": "LABSHPZAA156NRUG",
     }
 
     # pull series of interest using pandas_datareader
-    # fred_data = web.DataReader(fred_variable_dict.values(), "fred", start, end)
-    # fred_data.rename(
-    #     columns=dict((y, x) for x, y in fred_variable_dict.items()),
-    #     inplace=True,
-    # )
+    fred_data = web.DataReader(fred_variable_dict.values(), "fred", start, end)
+    fred_data.rename(
+        columns=dict((y, x) for x, y in fred_variable_dict.items()),
+        inplace=True,
+    )
+    # make data quarterly
+    fred_data_q = fred_data.resample("Q").mean()
 
     # Separate quartely, monthly, and annual FRED dataseries
 
@@ -180,7 +184,7 @@ def get_macro_params():
     # ]
 
     # find alpha_G
-    macro_parameters["alpha_G"] = [0.27]
+    # macro_parameters["alpha_G"] = [0.27]
     # macro_parameters["alpha_G"] = [
     #     pd.Series(
     #         (
@@ -221,7 +225,7 @@ def get_macro_params():
     1) Generate modelled corporate yields (corp_yhat) for a range of sovereign yields (sov_y)  using the estimated equation in col 2 of table 8 (and figure 3).
     2) Estimate the OLS using sovereign yields as the dependent variable
     """
-    """
+
     # # estimate r_gov_shift and r_gov_scale
     sov_y = np.arange(20, 120) / 10
     corp_yhat = 8.199 - (2.975 * sov_y) + (0.478 * sov_y**2)
@@ -232,10 +236,14 @@ def get_macro_params():
     )
     res = mod.fit()
     # first term is the constant and needs to be divided by 100 to have the correct unit. Second term is the coefficient
-    macro_parameters["r_gov_shift"] = (-res.params[0] / 100)  # constant = 0.0337662504
-    macro_parameters["r_gov_scale"] = res.params[1]  # coefficient = 0.24484764
-    """
-    macro_parameters["r_gov_shift"] = [-0.0337662504]
-    macro_parameters["r_gov_scale"] = [0.24484764]
+    macro_parameters["r_gov_shift"] = [
+        (-res.params[0] / 100)
+    ]  # constant = 0.0337662504
+    macro_parameters["r_gov_scale"] = [
+        res.params[1]
+    ]  # coefficient = 0.24484764
+
+    # macro_parameters["r_gov_shift"] = [-0.0337662504]
+    # macro_parameters["r_gov_scale"] = [0.24484764]
 
     return macro_parameters