"""
This module uses data from World Bank WDI, World Bank Quarterly Public
Sector Debt (QPSD) database, the IMF, and UN ILO to find values for
parameters for the OG-ZAF model that rely on macro data for calibration.
"""
# imports
from pandas_datareader import wb
import pandas as pd
import numpy as np
import requests
import datetime
import statsmodels.api as sm
from io import StringIO
[docs]
def get_macro_params(
data_start_date=datetime.datetime(1947, 1, 1),
data_end_date=datetime.datetime(2024, 12, 31),
country_iso="ZAF",
update_from_api=False,
):
"""
Compute values of parameters that are derived from macro data
Args:
data_start_date (datetime): start date for data
data_end_date (datetime): end date for data
country_iso (str): ISO code for country
Returns:
macro_parameters (dict): dictionary of parameter values
"""
# initialize a dictionary of parameters
macro_parameters = {}
# baseline date formatted for World Bank data
baseline_YYYYQ = (
str(data_end_date.year)
+ "Q"
+ str(pd.Timestamp(data_end_date).quarter)
)
"""
Retrieve data from the World Bank World Development Indicators.
"""
# Dictionaries of variables and their corresponding World Bank codes
# Annual data
wb_a_variable_dict = {
"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",
}
# Quarterly data
wb_q_variable_dict = {
"Gross PSD USD - domestic creditors": "DP.DOD.DECD.CR.PS.CD",
"Gross PSD USD - external creditors": "DP.DOD.DECX.CR.PS.CD",
"Gross PSD Gen Gov - percentage of GDP": "DP.DOD.DECT.CR.GG.Z1",
}
if update_from_api:
try:
# pull series of interest from the WB using pandas_datareader
# Annual data
wb_data_a = wb.download(
indicator=wb_a_variable_dict.values(),
country=country_iso,
start=data_start_date,
end=data_end_date,
)
wb_data_a.rename(
columns=dict((y, x) for x, y in wb_a_variable_dict.items()),
inplace=True,
)
# Quarterly data
wb_data_q = wb.download(
indicator=wb_q_variable_dict.values(),
country=country_iso,
start=data_start_date,
end=data_end_date,
)
wb_data_q.rename(
columns=dict((y, x) for x, y in wb_q_variable_dict.items()),
inplace=True,
)
# Remove the hierarchical index (country and year) of
# wb_data_q and create a single row index using year
wb_data_q = wb_data_q.reset_index()
wb_data_q = wb_data_q.set_index("year")
# Function to get the latest valid data if baseline_YYYYQ is missing or NaN
def get_valid_data(series, baseline_YYYYQ):
value = series.get(baseline_YYYYQ, None)
if pd.isna(value):
latest_non_nan = series.dropna().last_valid_index()
if latest_non_nan is not None:
print(
f"Warning: No data for {baseline_YYYYQ}. Using last available quarter: {latest_non_nan}"
)
value = series.get(latest_non_nan, None)
else:
print(
"Warning: No historical data available. Skipping update."
)
value = None
return value
# Compute macro parameters from WB data
macro_parameters["initial_debt_ratio"] = get_valid_data(
pd.Series(wb_data_q["Gross PSD Gen Gov - percentage of GDP"])
/ 100,
baseline_YYYYQ,
)
print(
f"initial_debt_ratio updated from World Bank API: {macro_parameters['initial_debt_ratio']}"
)
# Compute initial_foreign_debt_ratio safely
if (
"Gross PSD USD - external creditors" in wb_data_q.columns
and "Gross PSD USD - domestic creditors" in wb_data_q.columns
):
total_debt = (
wb_data_q["Gross PSD USD - domestic creditors"]
+ wb_data_q["Gross PSD USD - external creditors"]
)
# Avoid division by zero
wb_data_q["foreign_debt_ratio"] = wb_data_q[
"Gross PSD USD - external creditors"
] / total_debt.replace(0, np.nan)
macro_parameters["initial_foreign_debt_ratio"] = (
get_valid_data(
wb_data_q["foreign_debt_ratio"], baseline_YYYYQ
)
)
else:
print(
"Warning: Missing debt variables in World Bank data. Skipping update for initial_foreign_debt_ratio."
)
print(
f"initial_foreign_debt_ratio updated from World Bank API: {macro_parameters['initial_foreign_debt_ratio']}"
)
# Compute zeta_D safely
macro_parameters["zeta_D"] = [
macro_parameters["initial_foreign_debt_ratio"]
] # Since it's the same formula, we use the same calculated value
print(
f"zeta_D updated from World Bank API: {macro_parameters['zeta_D']}"
)
# Compute annual GDP growth safely
if "GDP per capita (constant 2015 US$)" in wb_data_a.columns:
g_y_series = wb_data_a[
"GDP per capita (constant 2015 US$)"
].pct_change(-1)
# If all values are NaN, return None
macro_parameters["g_y_annual"] = (
g_y_series.mean() if not g_y_series.isna().all() else None
)
else:
print(
"Warning: Missing GDP per capita data in World Bank data. Skipping update for g_y_annual."
)
print(
f"g_y_annual updated from World Bank API: {macro_parameters['g_y_annual']}"
)
except:
print("Failed to retrieve data from World Bank")
print("Will not update the following parameters:")
print(
"[initial_debt_ratio, initial_foreign_debt_ratio, zeta_D, g_y]"
)
else:
print("Not updating from World Bank API")
"""
Retrieve labour share data from the United Nations ILOSTAT Data API
(see https://rshiny.ilo.org/dataexplorer9/?lang=en)
The series code is SDG_1041_NOC_RT_A (capital share)
Labor share (gamma) = 1 - capital share
If this fails we will not update gamma in 'default_parameters.json'
"""
if update_from_api:
try:
target = (
"https://rplumber.ilo.org/data/indicator/"
+ "?id=SDG_1041_NOC_RT_A"
+ "&ref_area="
+ str(country_iso)
+ "&timefrom="
+ str(data_start_date.year)
+ "&timeto="
+ str(data_end_date.year)
+ "&type=both&format=.csv"
)
# Add headers
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
}
print("Attempting to update gamma from ILOSTAT")
response = requests.get(target, headers=headers)
if response.status_code != 200:
print(f"Error: Received status code {response.status_code}")
else:
print("Request successful.")
csv_content = StringIO(response.text)
df_temp = pd.read_csv(csv_content)
ilo_data = df_temp[["time", "obs_value"]]
# find gamma, capital's share of income
macro_parameters["gamma"] = [
1
- (
(
ilo_data.loc[
ilo_data["time"] == data_end_date.year, "obs_value"
].squeeze()
)
/ 100
)
]
print(
f"gamma updated from ILOSTAT API: {macro_parameters['gamma']}"
)
except:
print("Failed to retrieve data from ILOSTAT")
print("Will not update gamma")
else:
print("Not updating from ILOSTAT API")
"""
Calibrate parameters from IMF data
"""
if update_from_api:
# alpha_T, non-social security benefits as a fraction of GDP
# source: https://data.imf.org/?sk=78d0bcc1-7a8f-44eb-8a2c-d4e472b8e64b&hide_uv=1
# alpha_T = Employment-related social benefits expense - Social security benefits expense
macro_parameters["alpha_T"] = [0.36 - 0.0] # 2022 = 0.36
# alpha_G, gov't consumption expenditures as a fraction of GDP
# source: https://data.imf.org/?sk=23ca1c1d-e6a5-4f18-bc2e-7e215837f971&hide_uv=1
# alpha_G = Expense - Interest expense - Social benefits expense
macro_parameters["alpha_G"] = [0.324 - 0.047 - 0.036] # 2022 = 0.241
""""
Esimate the discount on sovereign yields relative to private debt
Follow the methodology in Li, Magud, Werner, Witte (2021)
available at:
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
discussion is here: https://github.com/EAPD-DRB/OG-ZAF/issues/22
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
"""
# # 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)
corp_yhat = sm.add_constant(corp_yhat)
mod = sm.OLS(
sov_y,
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
macro_parameters["r_gov_shift"] = [-res.params[0] / 100]
macro_parameters["r_gov_scale"] = [res.params[1]]
# Report new values
print(f"alpha_T updated from IMF data: {macro_parameters['alpha_T']}")
print(f"alpha_G updated from IMF data: {macro_parameters['alpha_G']}")
print(
f"r_gov_shift updated from IMF data: {macro_parameters['r_gov_shift']}"
)
print(
f"r_gov_scale updated from IMF data: {macro_parameters['r_gov_scale']}"
)
else:
print("Not updating alpha_T, alpha_G, r_gov_shift, r_gov_scale")
return macro_parameters
