def test_constant_demographics_TPI():
'''
This tests solves the model under the assumption of constant
demographics, a balanced budget, and tax functions that do not vary
over time.
In this case, given how initial guesss for the time
path are made, the time path should be solved for on the first
iteration and the values all along the time path should equal their
steady-state values.
'''
output_base = "./OUTPUT"
baseline_dir = "./OUTPUT"
# Create output directory structure
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [ss_dir, tpi_dir]
for _dir in dirs:
try:
print("making dir: ", _dir)
os.makedirs(_dir)
except OSError:
pass
spec = Specifications(run_micro=False, output_base=output_base,
baseline_dir=baseline_dir, test=False,
time_path=True, baseline=True, reform={},
guid='')
user_params = {'constant_demographics': True,
'budget_balance': True,
'zero_taxes': True,
'maxiter': 2,
'eta': (spec.omega_SS.reshape(spec.S, 1) *
spec.lambdas.reshape(1, spec.J))}
spec.update_specifications(user_params)
spec.get_tax_function_parameters(None, False)
# Run SS
ss_outputs = SS.run_SS(spec, None)
# save SS results
utils.mkdirs(os.path.join(baseline_dir, "SS"))
ss_dir = os.path.join(baseline_dir, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
# Run TPI
tpi_output = TPI.run_TPI(spec, None)
assert(np.allclose(tpi_output['bmat_splus1'][:spec.T, :, :],
ss_outputs['bssmat_splus1']))
def runner(output_base,
baseline_dir,
test=False,
time_path=True,
baseline=True,
reform={},
user_params={},
guid='',
run_micro=True,
data=None,
client=None,
num_workers=1):
'''
This function runs the OG-India model, solving for the steady-state
and (optionally) the time path equilibrium.
Args:
output_base (str): path to save output to
baseline_dir (str): path where baseline model results are saved
test (bool): whether to run model in test mode (which has
a smaller state space and higher tolerances for solution)
time_path (bool): whether to solve for the time path equlibrium
baseline (bool): whether the model run is the baseline run
reform (dict): Tax-Calculator policy dictionary
user_params (dict): dictionary with updates to default
parameters in OG-India
guid (str): id for OG-India run
run_micro (bool): whether to estimate tax functions from micro
data or load saved parameters from pickle file
data (str or Pandas DataFrame): path to or data to use in
Tax-Calculator
client (Dask client object): client
num_workers (int): number of workers to use for parallelization
with Dask
Returns:
None
'''
tick = time.time()
# Create output directory structure
ss_dir = os.path.join(output_base, "SS")
tpi_dir = os.path.join(output_base, "TPI")
dirs = [ss_dir, tpi_dir]
for _dir in dirs:
try:
print("making dir: ", _dir)
os.makedirs(_dir)
except OSError:
pass
print('In runner, baseline is ', baseline)
# Get parameter class
# Note - set run_micro false when initially load class
# Update later with call to spec.get_tax_function_parameters()
spec = Specifications(run_micro=False,
output_base=output_base,
baseline_dir=baseline_dir,
test=test,
time_path=time_path,
baseline=baseline,
reform=reform,
guid=guid,
data=data,
client=client,
num_workers=num_workers)
spec.update_specifications(user_params)
print('path for tax functions: ', spec.output_base)
spec.get_tax_function_parameters(client, run_micro)
'''
------------------------------------------------------------------------
Run SS
------------------------------------------------------------------------
'''
ss_outputs = SS.run_SS(spec, client=client)
'''
------------------------------------------------------------------------
Pickle SS results
------------------------------------------------------------------------
'''
if baseline:
utils.mkdirs(os.path.join(baseline_dir, "SS"))
ss_dir = os.path.join(baseline_dir, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
# Save pickle with parameter values for the run
param_dir = os.path.join(baseline_dir, "model_params.pkl")
pickle.dump(spec, open(param_dir, "wb"))
else:
utils.mkdirs(os.path.join(output_base, "SS"))
ss_dir = os.path.join(output_base, "SS/SS_vars.pkl")
pickle.dump(ss_outputs, open(ss_dir, "wb"))
# Save pickle with parameter values for the run
param_dir = os.path.join(output_base, "model_params.pkl")
pickle.dump(spec, open(param_dir, "wb"))
if time_path:
'''
------------------------------------------------------------------------
Run the TPI simulation
------------------------------------------------------------------------
'''
tpi_output = TPI.run_TPI(spec, client=client)
'''
------------------------------------------------------------------------
Pickle TPI results
------------------------------------------------------------------------
'''
tpi_dir = os.path.join(output_base, "TPI")
utils.mkdirs(tpi_dir)
tpi_vars = os.path.join(tpi_dir, "TPI_vars.pkl")
pickle.dump(tpi_output, open(tpi_vars, "wb"))
print("Time path iteration complete.")
print("It took {0} seconds to get that part done.".format(time.time() -
tick))