def test_macro_table(base_tpi, base_params, reform_tpi, reform_params,
output_type):
df = output_tables.macro_table(
base_tpi, base_params, reform_tpi=reform_tpi,
reform_params=reform_params, output_type=output_type,
include_SS=True, include_overall=True)
assert isinstance(df, pd.DataFrame)
def run_micro_macro(og_spec, guid, client):
guid = ''
start_time = time.time()
REFORM_DIR = os.path.join(CUR_PATH, "OUTPUT_REFORM_" + guid)
BASELINE_DIR = os.path.join(CUR_PATH, "OUTPUT_BASELINE" + guid)
with open("log_{}.log".format(guid), 'w') as f:
f.write("guid: {}\n".format(guid))
f.write("og_spec: {}\n".format(og_spec))
'''
------------------------------------------------------------------------
Run baseline
------------------------------------------------------------------------
'''
p = Specifications(
baseline=True, num_workers=NUM_WORKERS,
baseline_dir=BASELINE_DIR, output_base=BASELINE_DIR)
p.update_specifications(og_spec)
runner(p, time_path=True, client=client)
'''
------------------------------------------------------------------------
Run reform
------------------------------------------------------------------------
'''
p = Specifications(
baseline=False, num_workers=NUM_WORKERS,
baseline_dir=BASELINE_DIR, output_base=REFORM_DIR)
p.update_specifications(og_spec)
runner(p, time_path=True, client=client)
time.sleep(0.5)
base_tpi = safe_read_pickle(
os.path.join(BASELINE_DIR, 'TPI', 'TPI_vars.pkl'))
base_params = safe_read_pickle(
os.path.join(BASELINE_DIR, 'model_params.pkl'))
reform_tpi = safe_read_pickle(
os.path.join(REFORM_DIR, 'TPI', 'TPI_vars.pkl'))
reform_params = safe_read_pickle(
os.path.join(REFORM_DIR, 'model_params.pkl'))
ans = ot.macro_table(
base_tpi, base_params, reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['Y', 'C', 'K', 'L', 'r', 'w'], output_type='pct_diff',
num_years=10, start_year=base_params.start_year)
print("total time was ", (time.time() - start_time))
return ans
def comp_output(
base_params,
base_ss,
reform_params,
reform_ss,
time_path,
base_tpi=None,
reform_tpi=None,
var="cssmat",
):
"""
Function to create output for the COMP platform
"""
if time_path:
table_title = "Percentage Changes in Economic Aggregates Between"
table_title += " Baseline and Reform Policy"
plot1_title = "Pct Changes in Economic Aggregates Between"
plot1_title += " Baseline and Reform Policy"
plot2_title = "Pct Changes in Interest Rates and Wages"
plot2_title += " Between Baseline and Reform Policy"
plot3_title = "Differences in Fiscal Variables Relative to GDP"
plot3_title += " Between Baseline and Reform Policy"
out_table = ot.tp_output_dump_table(
base_params,
base_tpi,
reform_params,
reform_tpi,
table_format="csv",
)
html_table = ot.macro_table(
base_tpi,
base_params,
reform_tpi,
reform_params,
var_list=["Y", "C", "I_total", "L", "D", "G", "r", "w"],
output_type="pct_diff",
num_years=10,
include_SS=True,
include_overall=True,
start_year=base_params.start_year,
table_format="html",
)
fig1 = op.plot_aggregates(
base_tpi,
base_params,
reform_tpi,
reform_params,
var_list=["Y", "C", "K", "L"],
plot_type="pct_diff",
num_years_to_plot=50,
start_year=base_params.start_year,
vertical_line_years=[
base_params.start_year + base_params.tG1,
base_params.start_year + base_params.tG2,
],
plot_title=None,
path=None,
)
in_memory_file1 = io.BytesIO()
fig1.savefig(in_memory_file1, format="png", bbox_inches="tight")
in_memory_file1.seek(0)
fig2 = op.plot_aggregates(
base_tpi,
base_params,
reform_tpi,
reform_params,
var_list=["r_gov", "w"],
plot_type="pct_diff",
num_years_to_plot=50,
start_year=base_params.start_year,
vertical_line_years=[
base_params.start_year + base_params.tG1,
base_params.start_year + base_params.tG2,
],
plot_title=None,
path=None,
)
in_memory_file2 = io.BytesIO()
fig2.savefig(in_memory_file2, format="png", bbox_inches="tight")
in_memory_file2.seek(0)
fig3 = op.plot_gdp_ratio(
base_tpi,
base_params,
reform_tpi,
reform_params,
var_list=["D", "G", "total_tax_revenue"],
plot_type="diff",
num_years_to_plot=50,
start_year=base_params.start_year,
vertical_line_years=[
base_params.start_year + base_params.tG1,
base_params.start_year + base_params.tG2,
],
plot_title=None,
path=None,
)
in_memory_file3 = io.BytesIO()
fig3.savefig(in_memory_file3, format="png", bbox_inches="tight")
in_memory_file3.seek(0)
comp_dict = {
"renderable": [
{
"media_type": "PNG",
"title": plot1_title,
"data": in_memory_file1.read(),
},
{
"media_type": "PNG",
"title": plot2_title,
"data": in_memory_file2.read(),
},
{
"media_type": "PNG",
"title": plot3_title,
"data": in_memory_file3.read(),
},
{
"media_type": "table",
"title": table_title,
"data": html_table,
},
],
"downloadable": [
{
"media_type": "CSV",
"title": table_title,
"data": out_table.to_csv(),
}
],
}
else:
table_title = "Percentage Changes in Economic Aggregates Between"
table_title += " Baseline and Reform Policy"
plot_title = "Percentage Changes in Consumption by Lifetime Income"
plot_title += " Percentile Group"
out_table = ot.macro_table_SS(
base_ss,
reform_ss,
var_list=[
"Yss",
"Css",
"Iss_total",
"Gss",
"total_tax_revenue",
"Lss",
"rss",
"wss",
],
table_format="csv",
)
html_table = ot.macro_table_SS(
base_ss,
reform_ss,
var_list=[
"Yss",
"Css",
"Iss_total",
"Gss",
"total_tax_revenue",
"Lss",
"rss",
"wss",
],
table_format="html",
)
fig = op.ability_bar_ss(
base_ss, base_params, reform_ss, reform_params, var=var
)
in_memory_file = io.BytesIO()
fig.savefig(in_memory_file, format="png", bbox_inches="tight")
in_memory_file.seek(0)
comp_dict = {
"renderable": [
{
"media_type": "PNG",
"title": plot_title,
"data": in_memory_file.read(),
},
{
"media_type": "table",
"title": table_title,
"data": html_table,
},
],
"downloadable": [
{
"media_type": "CSV",
"title": table_title,
"data": out_table.to_csv(),
}
],
}
return comp_dict
def main():
# Define parameters to use for multiprocessing
client = Client()
num_workers = min(multiprocessing.cpu_count(), 7)
print("Number of workers = ", num_workers)
# Directories to save data
CUR_DIR = os.path.dirname(os.path.realpath(__file__))
base_dir = os.path.join(CUR_DIR, "OG-USA-Example", "OUTPUT_BASELINE")
reform_dir = os.path.join(CUR_DIR, "OG-USA-Example", "OUTPUT_REFORM")
"""
------------------------------------------------------------------------
Run baseline policy
------------------------------------------------------------------------
"""
# Set up baseline parameterization
p = Specifications(
baseline=True,
num_workers=num_workers,
baseline_dir=base_dir,
output_base=base_dir,
)
# Update parameters for baseline from default json file
p.update_specifications(
json.load(
open(
os.path.join(CUR_DIR, "..", "ogusa",
"ogusa_default_parameters.json"))))
# Run model
start_time = time.time()
runner(p, time_path=True, client=client)
print("run time = ", time.time() - start_time)
"""
------------------------------------------------------------------------
Run reform policy
------------------------------------------------------------------------
"""
# Grab a reform JSON file already in Tax-Calculator
# In this example the 'reform' is a change to 2017 law (the
# baseline policy is tax law in 2018)
reform_url = ("github://*****:*****@main/psl_examples/" +
"taxcalc/2017_law.json")
ref = Calculator.read_json_param_objects(reform_url, None)
iit_reform = ref["policy"]
# create new Specifications object for reform simulation
p2 = Specifications(
baseline=False,
num_workers=num_workers,
baseline_dir=base_dir,
output_base=reform_dir,
)
# Update parameters for baseline from default json file
p2.update_specifications(
json.load(
open(
os.path.join(CUR_DIR, "..", "ogusa",
"ogusa_default_parameters.json"))))
# Use calibration class to estimate reform tax functions from
# Tax-Calculator, specifing reform for Tax-Calculator in iit_reform
c2 = Calibration(p2,
iit_reform=iit_reform,
estimate_tax_functions=True,
client=client)
# update tax function parameters in Specifications Object
d = c2.get_dict()
# additional parameters to change
updated_params = {
"cit_rate": [0.35],
"etr_params": d["etr_params"],
"mtrx_params": d["mtrx_params"],
"mtry_params": d["mtry_params"],
"mean_income_data": d["mean_income_data"],
"frac_tax_payroll": d["frac_tax_payroll"],
}
p2.update_specifications(updated_params)
# Run model
start_time = time.time()
runner(p2, time_path=True, client=client)
print("run time = ", time.time() - start_time)
client.close()
"""
------------------------------------------------------------------------
Save some results of simulations
------------------------------------------------------------------------
"""
base_tpi = safe_read_pickle(os.path.join(base_dir, "TPI", "TPI_vars.pkl"))
base_params = safe_read_pickle(os.path.join(base_dir, "model_params.pkl"))
reform_tpi = safe_read_pickle(
os.path.join(reform_dir, "TPI", "TPI_vars.pkl"))
reform_params = safe_read_pickle(
os.path.join(reform_dir, "model_params.pkl"))
ans = ot.macro_table(
base_tpi,
base_params,
reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=["Y", "C", "K", "L", "r", "w"],
output_type="pct_diff",
num_years=10,
start_year=base_params.start_year,
)
# create plots of output
op.plot_all(base_dir, reform_dir,
os.path.join(CUR_DIR, "OG-USA_example_plots"))
print("Percentage changes in aggregates:", ans)
# save percentage change output to csv file
ans.to_csv("ogusa_example_output.csv")
def main():
# Define parameters to use for multiprocessing
client = Client()
num_workers = min(multiprocessing.cpu_count(), 7)
print('Number of workers = ', num_workers)
run_start_time = time.time()
# Directories to save data
CUR_DIR = os.path.dirname(os.path.realpath(__file__))
base_dir = os.path.join(CUR_DIR, BASELINE_DIR)
reform_dir = os.path.join(CUR_DIR, REFORM_DIR)
# Set some OG model parameters
# See default_parameters.json for more description of these parameters
alpha_T = np.zeros(50) # Adjusting the path of transfer spending
alpha_T[0:2] = 0.09
alpha_T[2:10] = 0.09 + 0.01
alpha_T[10:40] = 0.09 - 0.01
alpha_T[40:] = 0.09
alpha_G = np.zeros(7) # Adjusting the path of non-transfer spending
alpha_G[0:3] = 0.05 - 0.01
alpha_G[3:6] = 0.05 - 0.005
alpha_G[6:] = 0.05
# Set start year for baseline and reform.
START_YEAR = 2021
# Also adjust the Frisch elasticity, the start year, the
# effective corporate income tax rate, and the SS debt-to-GDP ratio
og_spec = {
'frisch': 0.41,
'start_year': START_YEAR,
'cit_rate': [0.21],
'debt_ratio_ss': 1.0,
'alpha_T': alpha_T.tolist(),
'alpha_G': alpha_G.tolist()
}
'''
------------------------------------------------------------------------
Run baseline policy first
------------------------------------------------------------------------
'''
p = Specifications(
baseline=True,
num_workers=num_workers,
baseline_dir=base_dir,
output_base=base_dir,
)
# Update parameters for baseline from default json file
p.update_specifications(og_spec)
start_time = time.time()
runner(p, time_path=True, client=client)
print('run time = ', time.time() - start_time)
'''
------------------------------------------------------------------------
Run reform policy
------------------------------------------------------------------------
'''
# update the effective corporate income tax rate
og_spec.update({'cit_rate': [0.35]})
p2 = Specifications(
baseline=False,
num_workers=num_workers,
baseline_dir=base_dir,
output_base=reform_dir,
)
# Update parameters for baseline from default json file
p2.update_specifications(og_spec)
start_time = time.time()
runner(p2, time_path=True, client=client)
print('run time = ', time.time() - start_time)
# return ans - the percentage changes in macro aggregates and prices
# due to policy changes from the baseline to the reform
base_tpi = safe_read_pickle(os.path.join(base_dir, 'TPI', 'TPI_vars.pkl'))
base_params = safe_read_pickle(os.path.join(base_dir, 'model_params.pkl'))
reform_tpi = safe_read_pickle(
os.path.join(reform_dir, 'TPI', 'TPI_vars.pkl'))
reform_params = safe_read_pickle(
os.path.join(reform_dir, 'model_params.pkl'))
ans = ot.macro_table(base_tpi,
base_params,
reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['Y', 'C', 'K', 'L', 'r', 'w'],
output_type='pct_diff',
num_years=10,
start_year=og_spec['start_year'])
# create plots of output
op.plot_all(base_dir, reform_dir, os.path.join(CUR_DIR,
'run_example_plots'))
print("total time was ", (time.time() - run_start_time))
print('Percentage changes in aggregates:', ans)
# save percentage change output to csv file
ans.to_csv('ogcore_example_output.csv')
client.close()
def main(reform=None):
if reform is None:
reform = get_default_reform()
# Define parameters to use for multiprocessing
client = Client()
num_workers = min(multiprocessing.cpu_count(), 7)
print("Number of workers = ", num_workers)
# Directories to save data
CUR_DIR = os.path.dirname(os.path.realpath(__file__))
base_dir = os.path.join(CUR_DIR, "OG-UK-Example", "OUTPUT_BASELINE")
reform_dir = os.path.join(CUR_DIR, "OG-UK-Example", "OUTPUT_REFORM")
"""
------------------------------------------------------------------------
Run baseline policy
------------------------------------------------------------------------
"""
# Set up baseline parameterization
p = Specifications(
baseline=True,
num_workers=num_workers,
baseline_dir=base_dir,
output_base=base_dir,
)
# Update parameters for baseline from default json file
p.update_specifications(
json.load(
open(
os.path.join(CUR_DIR, "..", "oguk",
"oguk_default_parameters.json"))))
# specify tax function form and start year
p.update_specifications({
"tax_func_type": "DEP",
"age_specific": False,
"start_year": START_YEAR,
"alpha_T": [5e-3],
"alpha_G": [5e-3],
})
# Estimate baseline tax functions from OpenFisca-UK
c = Calibration(p, estimate_tax_functions=True, client=client)
# update tax function parameters in Specifications Object
d = c.get_dict()
updated_params = {
"etr_params": d["etr_params"],
"mtrx_params": d["mtrx_params"],
"mtry_params": d["mtry_params"],
"mean_income_data": d["mean_income_data"],
"frac_tax_payroll": d["frac_tax_payroll"],
}
p.update_specifications(updated_params)
# Run model
start_time = time.time()
runner(p, time_path=True, client=client)
print("run time = ", time.time() - start_time)
"""
------------------------------------------------------------------------
Run reform policy
------------------------------------------------------------------------
"""
# create new Specifications object for reform simulation
p2 = Specifications(
baseline=False,
num_workers=num_workers,
baseline_dir=base_dir,
output_base=reform_dir,
)
# Update parameters for baseline from default json file
p2.update_specifications(
json.load(
open(
os.path.join(CUR_DIR, "..", "oguk",
"oguk_default_parameters.json"))))
# specify tax function form and start year
p2.update_specifications({
"tax_func_type": "DEP",
"age_specific": False,
"start_year": START_YEAR,
"alpha_T": [5e-3],
"alpha_G": [5e-3],
})
# Estimate reform tax functions from OpenFisca-UK, passing Reform
# class object
c2 = Calibration(p2,
iit_reform=reform,
estimate_tax_functions=True,
client=client)
# update tax function parameters in Specifications Object
d2 = c2.get_dict()
updated_params2 = {
"etr_params": d2["etr_params"],
"mtrx_params": d2["mtrx_params"],
"mtry_params": d2["mtry_params"],
"mean_income_data": d2["mean_income_data"],
"frac_tax_payroll": d2["frac_tax_payroll"],
}
p2.update_specifications(updated_params2)
# Run model
start_time = time.time()
runner(p2, time_path=True, client=client)
print("run time = ", time.time() - start_time)
"""
------------------------------------------------------------------------
Save some results of simulations
------------------------------------------------------------------------
"""
base_tpi = safe_read_pickle(os.path.join(base_dir, "TPI", "TPI_vars.pkl"))
base_params = safe_read_pickle(os.path.join(base_dir, "model_params.pkl"))
reform_tpi = safe_read_pickle(
os.path.join(reform_dir, "TPI", "TPI_vars.pkl"))
reform_params = safe_read_pickle(
os.path.join(reform_dir, "model_params.pkl"))
ans = ot.macro_table(
base_tpi,
base_params,
reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=["Y", "C", "K", "L", "r", "w"],
output_type="pct_diff",
num_years=10,
start_year=base_params.start_year,
)
# create plots of output
op.plot_all(base_dir, reform_dir,
os.path.join(CUR_DIR, "OG-UK_example_plots"))
print("Percentage changes in aggregates:", ans)
# save percentage change output to csv file
ans.to_csv("oguk_example_output.csv")
client.close()