Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update macro parameters #57

Merged
merged 4 commits into from
Jul 28, 2024
Merged

Update macro parameters #57

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
33a927d
use IMF data for alpha_T and alpha_G
jdebacker Jul 24, 2024
e396c1f
update RC tolerance for TPI
jdebacker Jul 24, 2024
cbc17f9
default parameters update
jdebacker Jul 26, 2024
8060f56
update run script
jdebacker Jul 26, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 7 additions & 5 deletions examples/run_og_zaf.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -58,16 +58,18 @@ def main():
# Update parameters from calibrate.py Calibration class
c = Calibration(p)
updated_params = c.get_dict()
p.tax_func_type = "linear"
p.age_specific = False
p.update_specifications(updated_params)
# set underlying growth rate to zero, as value from data is negative
p.g_y = 0.0
# set tax rates
p.update_specifications(
{
"cit_rate": [[0.27]],
"tax_func_type": "linear",
"age_specific": False,
# "etr_params": [[[0.22]]],
# "mtrx_params": [[[0.31]]],
# "mtry_params": [[[0.25]]],
"etr_params": [[[0.22]]],
"mtrx_params": [[[0.31]]],
"mtry_params": [[[0.25]]],
"tau_c": [[0.15]],
}
)
Expand Down
86 changes: 18 additions & 68 deletions ogzaf/macro_params.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,6 @@
"""

# imports
import pandas_datareader.data as web
from pandas_datareader import wb
import pandas as pd
import numpy as np
Expand Down Expand Up @@ -111,35 +110,6 @@ def get_macro_params():

ilo_data = df_temp[["time", "obs_value"]]

"""
This retrieves data from FRED.
"""

fred_variable_dict = {
# "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,
)
# make data quarterly
fred_data_q = fred_data.resample("Q").mean()

# Separate quartely, monthly, and annual FRED dataseries

# fred_data_d = fred_data[["BAA Corp Bond Rates", "10 year govt bond rate"]]

# initialize a dictionary of parameters
macro_parameters = {}

Expand Down Expand Up @@ -171,32 +141,14 @@ def get_macro_params():
).loc[baseline_yearquarter]
]

# find alpha_T
macro_parameters["alpha_T"] = [0.04]
# macro_parameters["alpha_T"] = [
# pd.Series(
# (
# fred_data_q["Total gov transfer payments"]
# - fred_data_q["Social Security payments"]
# )
# / fred_data_q["Nominal GDP"]
# ).loc[baseline_date]
# ]

# find alpha_G
# macro_parameters["alpha_G"] = [0.27]
# macro_parameters["alpha_G"] = [
# pd.Series(
# (
# fred_data_q["Gov expenditures"]
# - fred_data_q["Total gov transfer payments"]
# - fred_data_q["Gov interest payments"]
# )
# / fred_data_q["Nominal GDP"]
# ).loc[baseline_date]
# ]

# find gamma
# alpha_T, non-social security benefits as a fraction of GDP
# source: https://data.imf.org/?sk=b052f0f0-c166-43b6-84fa-47cccae3e219&hide_uv=1
macro_parameters["alpha_T"] = [0.041 - 0.0]
# alpha_G, gov't consumption expenditures as a fraction of GDP
# source: https://data.imf.org/?sk=edb0cd70-0af3-40e1-a9c3-bdef83ee4d1e&hide_uv=1
macro_parameters["alpha_G"] = [0.351 - 0.043 - 0.041]

# find gamma, capital's share of income
macro_parameters["gamma"] = [
1
- (
Expand All @@ -207,10 +159,6 @@ def get_macro_params():
)
/ 100
)
# 1
# - pd.Series(
# (fred_data["Labor share of income"]).loc[baseline_yearquarter]
# ).mean()
]

# find g_y
Expand All @@ -219,11 +167,15 @@ def get_macro_params():
)

""""
We want to use the non linear relationship estimated by Li, Magud, Werner, Witte (2021), available here: https://www.imf.org/en/Publications/WP/Issues/2021/06/04/The-Long-Run-Impact-of-Sovereign-Yields-on-Corporate-Yields-in-Emerging-Markets-50224
We want to use the non linear relationship estimated by
Li, Magud, Werner, Witte (2021), available here:
https://www.imf.org/en/Publications/WP/Issues/2021/06/04/The-Long-Run-Impact-of-Sovereign-Yields-on-Corporate-Yields-in-Emerging-Markets-50224

Steps:
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
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
Expand All @@ -235,15 +187,13 @@ def get_macro_params():
corp_yhat,
)
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
# 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
] # 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
Loading
Loading