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try/except for wb api
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@jdebacker
jdebacker committed Aug 2, 2024
1 parent ab4cb25 commit 93724cf
Showing 1 changed file with 92 additions and 101 deletions.
193 changes: 92 additions & 101 deletions ogzaf/macro_params.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,81 +16,107 @@

def get_macro_params(
data_start_date=datetime.datetime(1947, 1, 1),
data_end_date=datetime.date.today()):
data_end_date=datetime.date.today(),
country_iso="ZAF"
):
"""
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
"""

# set beginning and end dates for data
# format is year (1940),month (1),day (1)
country_iso = "ZAF"
# initialize a dictionary of parameters
macro_parameters = {}
# baseline date formatted for World Bank data
baseline_YYYYQ = data_end_date.strftime("%YQ%q")

"""
This retrieves annual data from the World Bank World Development Indicators.
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",
# "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",
}
# 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",
}

# pull series of interest using pandas_datareader
# wb_data_a = wb.download(
# indicator=wb_a_variable_dict.values(),
# country=country_iso,
# start=data_start_date,
# end=end,
# )
# wb_data_a.rename(
# columns=dict((y, x) for x, y in wb_a_variable_dict.items()),
# inplace=True,
# )

"""
This retrieves quarterly data from the World Bank Quarterly Public Sector Debt database.
The command extracts all available data even when start and end dates are specified.
"""

# 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",
# }

# pull series of interest using pandas_datareader
# wb_data_q = wb.download(
# indicator=wb_q_variable_dict.values(),
# country=country_iso,
# start=data_start_date,
# end=end,
# )
# 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")
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")

# Compute macro parameters from WB data
macro_parameters["initial_debt_ratio"] = (
pd.Series(
wb_data_q["Gross PSD Gen Gov - percentage of GDP"]).loc[
baseline_YYYYQ]) / 100
macro_parameters["initial_foreign_debt_ratio"] = pd.Series(
wb_data_q["Gross PSD USD - external creditors"]
/ (
wb_data_q["Gross PSD USD - domestic creditors"]
+ wb_data_q["Gross PSD USD - external creditors"]
)
).loc[baseline_YYYYQ]
# zeta_D = share of new debt issues from government that are
# purchased by foreigners
macro_parameters["zeta_D"] = [
pd.Series(
wb_data_q["Gross PSD USD - external creditors"]
/ (
wb_data_q["Gross PSD USD - domestic creditors"]
+ wb_data_q["Gross PSD USD - external creditors"]
)
).loc[baseline_YYYYQ]
]
macro_parameters["g_y_annual"] = (
wb_data_a["GDP per capita (constant 2015 US$)"].pct_change(-1).mean()
)
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]")

"""
This retrieves labour share data from the ILOSTAT Data API
Retrieve labour share data from the United Nations ILOSTAT Data API
(see https://rshiny.ilo.org/dataexplorer9/?lang=en)
"""

target = (
"https://rplumber.ilo.org/data/indicator/"
+ "?id=LAP_2GDP_NOC_RT_A"
Expand All @@ -102,7 +128,6 @@ def get_macro_params(
+ str(data_end_date.year)
+ "&type=both&format=.csv"
)

response = requests.get(target)
if response.status_code == 200:
csv_content = StringIO(response.text)
Expand All @@ -111,68 +136,34 @@ def get_macro_params(
print(
f"Failed to retrieve data. HTTP status code: {response.status_code}"
)

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

# initialize a dictionary of parameters
macro_parameters = {}

# print(fred_data.loc(str(baseline_date)))
# find initial_debt_ratio
# macro_parameters["initial_debt_ratio"] = (
# pd.Series(wb_data_q["Gross PSD Gen Gov - percentage of GDP"]).loc[
# baseline_YYYYQ
# ]
# ) / 100

# find initial_foreign_debt_ratio
# macro_parameters["initial_foreign_debt_ratio"] = pd.Series(
# wb_data_q["Gross PSD USD - external creditors"]
# / (
# wb_data_q["Gross PSD USD - domestic creditors"]
# + wb_data_q["Gross PSD USD - external creditors"]
# )
# ).loc[baseline_YYYYQ]

# find zeta_D (Share of new debt issues from government that are purchased by foreigners)
# macro_parameters["zeta_D"] = [
# pd.Series(
# wb_data_q["Gross PSD USD - external creditors"]
# / (
# wb_data_q["Gross PSD USD - domestic creditors"]
# + wb_data_q["Gross PSD USD - external creditors"]
# )
# ).loc[baseline_YYYYQ]
# ]

# 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
- (
(
ilo_data.loc[
ilo_data["time"] == baseline_date.year, "obs_value"
ilo_data["time"] == data_end_date.year, "obs_value"
].squeeze()
)
/ 100
)
]

# find g_y
# macro_parameters["g_y_annual"] = (
# wb_data_a["GDP per capita (constant 2015 US$)"].pct_change(-1).mean()
# )
"""
Calibrate parameters from IMF data
"""
# 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]

""""
We want to use the non linear relationship estimated by
Li, Magud, Werner, Witte (2021), available here:
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
Steps:
Expand All @@ -191,13 +182,13 @@ def get_macro_params(
corp_yhat,
)
res = mod.fit()
# first term is the constant and needs to be divided by 100 to have
# 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
]

return macro_parameters

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