from ogusa import estimate_beta_j, bequest_transmission
from ogusa import macro_params, transfer_distribution, income
from ogusa import get_micro_data
import os
import numpy as np
from taxcalc import Records
from ogcore import txfunc, demographics
from ogcore.utils import safe_read_pickle, mkdirs
[docs]
class Calibration:
"""OG-USA calibration class"""
def __init__(
self,
p,
estimate_tax_functions=False,
estimate_beta=False,
estimate_chi_n=False,
estimate_pop=False,
tax_func_path=None,
iit_baseline=None,
iit_reform={},
guid="",
data="cps",
gfactors=None,
weights=None,
records_start_year=Records.CPSCSV_YEAR,
client=None,
num_workers=1,
demographic_data_path=None,
output_path=None,
):
"""
Constructor for the Calibration class. This class is used to find
parameter values for the OG-USA model.
Args:
p (OG-USA Parameters object): parameters object
estimate_tax_functions (bool): whether to estimate tax functions
estimate_beta (bool): whether to estimate beta
estimate_chi_n (bool): whether to estimate chi_n
estimate_pop (bool): whether to estimate population
tax_func_path (str): path to tax function parameters
iit_baseline (dict): baseline policy to use
iit_reform (dict): reform tax parameters
guid (str): id for tax function parameters
data (str or Pandas DataFrame): path or DataFrame with
data for Tax-Calculator model
gfactors (str or Pandas DataFrame ): path or DataFrame with
growth factors for Tax-Calculator model
weights (str or Pandas DataFrame): path or DataFrame with
weights for Tax-Calculator model
records_start_year (int): year micro data begins
client (Dask client object): client
num_workers (int): number of workers for Dask client
output_path (str): path to save output to
Returns:
Calibration class object instance
"""
# Create output_path if it doesn't exist
if output_path is not None:
if not os.path.exists(output_path):
os.makedirs(output_path)
self.estimate_tax_functions = estimate_tax_functions
self.estimate_beta = estimate_beta
self.estimate_chi_n = estimate_chi_n
self.estimate_pop = estimate_pop
if estimate_tax_functions:
if tax_func_path is not None:
run_micro = False
else:
run_micro = True
self.tax_function_params = self.get_tax_function_parameters(
p,
iit_baseline,
iit_reform,
guid,
data,
gfactors,
weights,
records_start_year,
client,
num_workers,
run_micro=run_micro,
tax_func_path=tax_func_path,
)
if self.estimate_beta:
self.beta_j = estimate_beta_j.beta_estimate(self)
# if estimate_chi_n:
# chi_n = self.get_chi_n()
# Macro estimation
self.macro_params = macro_params.get_macro_params()
# eta estimation
self.eta = transfer_distribution.get_transfer_matrix(
p.J, p.lambdas, output_path=output_path
)
# zeta estimation
self.zeta = bequest_transmission.get_bequest_matrix(
p.J, p.lambdas, output_path=output_path
)
# demographics
if estimate_pop:
self.demographic_params = demographics.get_pop_objs(
p.E,
p.S,
p.T,
0,
99,
initial_data_year=p.start_year - 1,
final_data_year=p.start_year,
GraphDiag=False,
download_path=demographic_data_path,
)
# demographics for 80 period lives (needed for getting e below)
demog80 = demographics.get_pop_objs(
20,
80,
p.T,
0,
99,
initial_data_year=p.start_year - 1,
final_data_year=p.start_year,
GraphDiag=False,
)
# earnings profiles
self.e = income.get_e_interp(
p.S,
self.demographic_params["omega_SS"],
demog80["omega_SS"],
p.lambdas,
plot_path=output_path,
)
else:
self.e = income.get_e_interp(
p.S,
p.omega_SS,
p.omega_SS,
p.lambdas,
plot_path=output_path,
)
# Tax Functions
[docs]
def get_tax_function_parameters(
self,
p,
iit_baseline=None,
iit_reform={},
guid="",
data="",
gfactors=None,
weights=None,
records_start_year=Records.CPSCSV_YEAR,
client=None,
num_workers=1,
run_micro=False,
tax_func_path=None,
):
"""
Reads pickle file of tax function parameters or estimates the
parameters from microsimulation model output.
Args:
p (OG-Core Parameters object): parameters object
iit_baseline (dict): baseline policy to use
iit_reform (dict): reform tax parameters
guid (string): id for tax function parameters
data (str or Pandas DataFrame): path or DataFrame with
data for Tax-Calculator model
gfactors (str or Pandas DataFrame ): path or DataFrame with
growth factors for Tax-Calculator model
weights (str or Pandas DataFrame): path or DataFrame with
weights for Tax-Calculator model
records_start_year (int): year micro data begins
client (Dask client object): client
num_workers (int): number of workers for Dask client
run_micro (bool): whether to estimate parameters from
microsimulation model
tax_func_path (string): path where find or save tax
function parameter estimates
Returns:
None
"""
# set paths if none given
if tax_func_path is None:
if p.baseline:
pckl = "TxFuncEst_baseline{}.pkl".format(guid)
tax_func_path = os.path.join(p.output_base, pckl)
print("Using baseline tax parameters from ", tax_func_path)
else:
pckl = "TxFuncEst_policy{}.pkl".format(guid)
tax_func_path = os.path.join(p.output_base, pckl)
print(
"Using reform policy tax parameters from ", tax_func_path
)
# create directory for tax function pickles to be saved to
mkdirs(os.path.split(tax_func_path)[0])
# If run_micro is false, check to see if parameters file exists
# and if it is consistent with Specifications instance
if not run_micro:
dict_params, run_micro = self.read_tax_func_estimate(
p, tax_func_path
)
taxcalc_version = "Cached tax parameters, no taxcalc version"
if run_micro:
micro_data, taxcalc_version = get_micro_data.get_data(
baseline=p.baseline,
start_year=p.start_year,
iit_baseline=iit_baseline,
iit_reform=iit_reform,
data=data,
gfactors=gfactors,
weights=weights,
path=p.output_base,
client=client,
num_workers=num_workers,
)
p.BW = len(micro_data)
dict_params = txfunc.tax_func_estimate( # pragma: no cover
micro_data,
p.BW,
p.S,
p.starting_age,
p.ending_age,
start_year=p.start_year,
analytical_mtrs=p.analytical_mtrs,
tax_func_type=p.tax_func_type,
age_specific=p.age_specific,
client=client,
num_workers=num_workers,
tax_func_path=tax_func_path,
)
mean_income_data = dict_params["tfunc_avginc"][0]
frac_tax_payroll = np.append(
dict_params["tfunc_frac_tax_payroll"],
np.ones(p.T + p.S - p.BW)
* dict_params["tfunc_frac_tax_payroll"][-1],
)
# Conduct checks to be sure tax function params are consistent
# with the model run
params_list = ["etr", "mtrx", "mtry"]
BW_in_tax_params = dict_params["BW"]
start_year_in_tax_params = dict_params["start_year"]
S_in_tax_params = len(dict_params["tfunc_etr_params_S"][0])
# Check that start years are consistent in model and cached tax functions
if p.start_year != start_year_in_tax_params:
print(
"Input Error: There is a discrepancy between the start"
+ " year of the model and that of the tax functions!!"
)
assert False
# Check that S is consistent in model and cached tax functions
# Note: even if p.age_specific = False, the arrays coming from
# ogcore.txfunc_est should be of length S
if p.S != S_in_tax_params:
print(
"Input Error: There is a discrepancy between the ages"
+ " used in the model and those in the tax functions!!"
)
assert False
# Extrapolate tax function parameters for years after budget window
# list of list: BW x S - either an array of function at that element...
etr_params = [[None] * p.S] * p.T
mtrx_params = [[None] * p.S] * p.T
mtry_params = [[None] * p.S] * p.T
for s in range(p.S):
for t in range(p.T):
if t < p.BW:
etr_params[t][s] = dict_params["tfunc_etr_params_S"][t][s]
mtrx_params[t][s] = dict_params["tfunc_mtrx_params_S"][t][
s
]
mtry_params[t][s] = dict_params["tfunc_mtry_params_S"][t][
s
]
else:
etr_params[t][s] = dict_params["tfunc_etr_params_S"][-1][s]
mtrx_params[t][s] = dict_params["tfunc_mtrx_params_S"][-1][
s
]
mtry_params[t][s] = dict_params["tfunc_mtry_params_S"][-1][
s
]
if p.constant_rates:
print("Using constant rates!")
# Make all tax rates equal the average
p.tax_func_type = "linear"
etr_params = [[None] * p.S] * p.T
mtrx_params = [[None] * p.S] * p.T
mtry_params = [[None] * p.S] * p.T
for s in range(p.S):
for t in range(p.T):
if t < p.BW:
etr_params[t][s] = dict_params["tfunc_avg_etr"][t]
mtrx_params[t][s] = dict_params["tfunc_avg_mtrx"][t]
mtry_params[t][s] = dict_params["tfunc_avg_mtry"][t]
else:
etr_params[t][s] = dict_params["tfunc_avg_etr"][-1]
mtrx_params[t][s] = dict_params["tfunc_avg_mtrx"][-1]
mtry_params[t][s] = dict_params["tfunc_avg_mtry"][-1]
if p.zero_taxes:
print("Zero taxes!")
etr_params = [[0] * p.S] * p.T
mtrx_params = [[0] * p.S] * p.T
mtry_params = [[0] * p.S] * p.T
tax_param_dict = {
"etr_params": etr_params,
"mtrx_params": mtrx_params,
"mtry_params": mtry_params,
"taxcalc_version": taxcalc_version,
"mean_income_data": mean_income_data,
"frac_tax_payroll": frac_tax_payroll,
}
return tax_param_dict
[docs]
def read_tax_func_estimate(self, p, tax_func_path):
"""
This function reads in tax function parameters from pickle
files.
Args:
tax_func_path (str): path to pickle with tax function
parameter estimates
Returns:
dict_params (dict): dictionary containing arrays of tax
function parameters
run_micro (bool): whether to estimate tax function parameters
"""
flag = 0
if os.path.exists(tax_func_path):
print("Tax Function Path Exists")
dict_params = safe_read_pickle(tax_func_path)
# check to see if tax_functions compatible
try:
if p.start_year != dict_params["start_year"]:
print(
"Model start year not consistent with tax "
+ "function parameter estimates"
)
flag = 1
except KeyError:
pass
try:
p.BW = dict_params["BW"] # QUICK FIX
if p.BW != dict_params["BW"]:
print(
"Model budget window length is "
+ str(p.BW)
+ " but the tax function parameter "
+ "estimates have a budget window length of "
+ str(dict_params["BW"])
)
flag = 1
except KeyError:
pass
try:
if p.tax_func_type != dict_params["tax_func_type"]:
print(
"Model tax function type is not "
+ "consistent with tax function parameter "
+ "estimates"
)
flag = 1
except KeyError:
pass
if flag >= 1:
raise RuntimeError(
"Tax function parameter estimates at given path"
+ " are not consistent with model parameters"
+ " specified."
)
else:
flag = 1
print(
"Tax function parameter estimates do not exist at"
+ " given path. Running new estimation."
)
if flag >= 1:
dict_params = None
run_micro = True
else:
run_micro = False
return dict_params, run_micro
# method to return all newly calibrated parameters in a dictionary
def get_dict(self):
dict = {}
if self.estimate_tax_functions:
dict.update(self.tax_function_params)
if self.estimate_beta:
dict["beta_annual"] = self.beta
if self.estimate_chi_n:
dict["chi_n"] = self.chi_n
dict["eta"] = self.eta
dict["zeta"] = self.zeta
dict.update(self.macro_params)
dict["e"] = self.e
if self.estimate_pop:
dict.update(self.demographic_params)
return dict
