def __init__(self,
btax_refdict,
iit_refdict,
btax_basedict={},
iit_basedict={},
elast_dict=None,
investor_data='puf.csv'):
# Set default policy parameters for later use
self.btax_defaults = Data().btax_defaults
# Create the baseline and reform parameter storing forms
self.btax_params_base = self.update_btax_params(btax_basedict)
self.btax_params_ref = self.update_btax_params(btax_refdict)
# Create Investors
self.investor_base = Investor(iit_basedict, investor_data)
self.investor_ref = Investor(iit_refdict, investor_data)
# Create Corporations
self.corp_base = Corporation(self.btax_params_base)
self.corp_ref = Corporation(self.btax_params_ref)
# Create PassThroughs
self.passthru_base = PassThrough(self.btax_params_base)
self.passthru_ref = PassThrough(self.btax_params_ref)
# Save the elasticity dictionary
if elast_dict is not None:
self.check_elast_dict(elast_dict)
self.elast_dict = elast_dict
else:
self.elast_dict = Data().elast_defaults
def __init__(self, btax_params, corp=True, data=None, response=None):
# Create an associated Data object
if isinstance(data, Data):
self.data = data
else:
self.data = Data()
# Check inputs
if isinstance(corp, bool):
self.corp = corp
else:
raise ValueError('corp must be True or False')
if response is None or isinstance(response, pd.DataFrame):
self.response = response
else:
raise ValueError('response must be DataFrame or None')
if corp:
self.adjustments = {
'bonus': 0.60290131,
'sec179': 0.016687178,
'overall': self.data.adjfactor_dep_corp,
'rescalar': self.data.rescale_corp
}
else:
self.adjustments = {
'bonus': 0.453683778,
'sec179': 0.17299506,
'overall': self.data.adjfactor_dep_noncorp,
'rescalar': self.data.rescale_noncorp
}
if isinstance(btax_params, pd.DataFrame):
self.btax_params = btax_params
else:
raise ValueError('btax_params must be DataFrame')
def calc_tauE(mtrdict, incdict, year):
"""
Calculate the effective marginal tax rate on equity income in year.
"""
# Retained earnings rate
m = 0.44
# Nominal expected return to equity
iyr = year - START_YEAR
E = (Data().econ_defaults['r_e_c'][iyr] +
Data().econ_defaults['pi'][iyr])
# shares of cg in short-term, long-term, and held until death
omega_scg = 0.034
omega_lcg = 0.496
omega_xcg = 1 - omega_scg - omega_lcg
# shares of corp equity in taxable, deferred and nontaxable form
alpha_ft = 0.572
alpha_td = 0.039
alpha_nt = 0.389
# holding period for equity
h_scg = 0.5
h_lcg = 8.0
h_td = 8.0
# Get MTRs
mtr_d = mtrdict['e00650']
mtr_scg = mtrdict['p22250']
mtr_lcg = mtrdict['p23250']
mtr_td = mtrdict['e01700']
# Get income measures
inc_d = incdict['div']
inc_scg = np.where(incdict['stcg'] >= 0, incdict['stcg'], 0)
inc_lcg = np.where(incdict['ltcg'] >= 0, incdict['ltcg'], 0)
inc_td = incdict['definc']
posti = (incdict['taxinc'] > 0.)
wgt = incdict['wgt']
# MTR on dividend income
tau_d = sum(mtr_d * inc_d * posti * wgt) / sum(inc_d * posti * wgt)
# accrual effective mtr on stcg
tau_scg1 = (sum(mtr_scg * inc_scg * posti * wgt) /
sum(inc_scg * posti * wgt))
tau_scg = (1 -
(np.log(np.exp(m * E * h_scg) * (1 - tau_scg1) + tau_scg1) /
(m * E * h_scg)))
# accrual effective mtr on ltcg
tau_lcg1 = (sum(mtr_lcg * inc_lcg * posti * wgt) /
sum(inc_lcg * posti * wgt))
tau_lcg = (1 -
(np.log(np.exp(m * E * h_lcg) * (1 - tau_lcg1) + tau_lcg1) /
(m * E * h_lcg)))
# mtr on capital gains held until death
tau_xcg = 0.0
tau_cg = (omega_scg * tau_scg + omega_lcg * tau_lcg +
omega_xcg * tau_xcg)
tau_ft = (1 - m) * tau_d + m * tau_cg
tau_td1 = (sum(mtr_td * inc_td * posti * wgt) /
sum(inc_td * posti * wgt))
tau_td = (1 - (np.log(np.exp(E * h_td) * (1 - tau_td1) + tau_td1) /
(E * h_td)))
tau_e = alpha_ft * tau_ft + alpha_td * tau_td + alpha_nt * 0.0
return tau_e
def __init__(self, btax_params):
# Store policy parameter objects
if isinstance(btax_params, pd.DataFrame):
self.btax_params = btax_params
else:
raise ValueError('btax_params must be DataFrame')
# Create Data object
self.data = Data()
# Create Asset object and calculate
self.asset = Asset(self.btax_params, corp=False, data=self.data)
self.asset.calc_all()
# Create earnings forecast
self.create_earnings()
def __init__(self, btax_params, earnings,
data=None, assets=None, debts=None):
# Create an associated Data object
if isinstance(data, Data):
self.data = data
else:
self.data = Data()
if isinstance(btax_params, pd.DataFrame):
self.btax_params = btax_params
else:
raise ValueError('btax_params must be DataFrame')
if assets is not None:
if isinstance(assets, Asset):
self.assets = assets
else:
raise ValueError('assets must be Asset object')
else:
self.assets = Asset(btax_params)
self.assets.calc_all()
if debts is not None:
if isinstance(debts, Debt):
self.debts = debts
else:
raise ValueError('debts must be Debt object')
else:
assets_forecast = self.assets.get_forecast()
self.debts = Debt(btax_params, assets_forecast)
self.debts.calc_all()
# Use earnings to create DataFrame for results
assert len(earnings) == 14
combined = pd.DataFrame({'year': range(2014,2028), 'ebitda': earnings})
# Add tax depreciation and net interest deductions
combined['taxDep'] = self.assets.get_taxdep()
combined['nid'] = self.debts.get_nid()
self.combined_return = combined
def _calc_investment_response(self, btax_params_base, btax_params_ref):
"""
Calculates percent change in investment & marginal product of capital,
for each asset type, for each year, corporate and noncorporate.
firstyear: when the firm behavioral response takes effect
"""
# Read in the underlying functions for the investment response
maindata = copy.deepcopy(Data().taxdep_info_gross('pre2017'))
maindata.drop(['L_gds', 'L_ads', 'Method'], axis=1, inplace=True)
# Extract relevant response parameters
firstyear = self.elasticities['first_year_response']
elast_c = self.elasticities['inv_usercost_c']
elast_nc = self.elasticities['inv_usercost_nc']
selast_c = self.elasticities['inv_eatr_c']
selast_nc = self.elasticities['inv_eatr_nc']
mne_share_c = self.elasticities['mne_share_c']
mne_share_nc = self.elasticities['mne_share_nc']
# No responses for years before first_year_response
for year in range(START_YEAR, firstyear):
ystr = str(year)
maindata['deltaIc' + ystr] = 0.
maindata['deltaInc' + ystr] = 0.
maindata['MPKc' + ystr] = 0.
maindata['MPKnc' + ystr] = 0.
# Calculate cost of capital and EATR for every year for baseline
btaxmini_base = BtaxMini(btax_params_base)
years = range(firstyear, END_YEAR + 1)
results_base = btaxmini_base.run_btax_mini(years)
# Calculate cost of capital and EATR for every year for reform
btaxmini_ref = BtaxMini(btax_params_ref)
results_ref = btaxmini_ref.run_btax_mini(years)
# Compare results to produce the responses
for year in years:
ystr = str(year)
maindata['deltaIc' + ystr] = (
((results_ref['u_c' + ystr] / results_base['u_c' + ystr] - 1) *
elast_c +
(results_ref['eatr_c' + ystr] - results_base['eatr_c' + ystr])
* selast_c * mne_share_c))
maindata['deltaInc' + ystr] = (
((results_ref['u_nc' + ystr] / results_base['u_nc' + ystr] - 1)
* elast_nc +
(results_ref['eatr_nc' + ystr] -
results_base['eatr_nc' + ystr]) * selast_nc * mne_share_nc))
maindata['MPKc' + ystr] = (results_ref['u_c' + ystr] +
results_base['u_c' + ystr]) / 2.0
maindata['MPKnc' + ystr] = (results_ref['u_nc' + ystr] +
results_base['u_nc' + ystr]) / 2.0
# Save the responses
self.investment_response = copy.deepcopy(maindata)
def calc_inv_response(self):
"""
Calculates the percent change in investment & marginal product of capital,
for each asset type, for each year, corporate and noncorporate.
firstyear: when the firm behavioral response takes effect
"""
# Read in the underlying functions for the investment response
maindata = copy.deepcopy(Data().assets_data())
maindata.drop(['assets_c', 'assets_nc'], axis=1, inplace=True)
# Extract relevant response parameters
firstyear = self.elast_dict['first_year_response']
elast_c = self.elast_dict['inv_usercost_c']
elast_nc = self.elast_dict['inv_usercost_nc']
selast_c = self.elast_dict['inv_eatr_c']
selast_nc = self.elast_dict['inv_eatr_nc']
mne_share_c = self.elast_dict['mne_share_c']
mne_share_nc = self.elast_dict['mne_share_nc']
# No responses for years before first_year_response
for year in range(2014, firstyear):
maindata['deltaIc' + str(year)] = 0.
maindata['deltaInc' + str(year)] = 0.
maindata['MPKc' + str(year)] = 0.
maindata['MPKnc' + str(year)] = 0.
# Calculate cost of capital and EATR for every year for baseline
Btax_base = BtaxMini(self.btax_params_base)
results_base = Btax_base.run_btax_mini(range(firstyear, 2028))
# Calculate cost of capital and EATR for every year for reform
Btax_ref = BtaxMini(self.btax_params_ref)
results_ref = Btax_ref.run_btax_mini(range(firstyear, 2028))
# Compare results to produce the responses
for year in range(firstyear, 2028):
maindata['deltaIc' + str(year)] = (
(results_ref['u_c' + str(year)] /
results_base['u_c' + str(year)] - 1) * elast_c +
(results_ref['eatr_c' + str(year)] -
results_base['eatr_c' + str(year)]) * selast_c * mne_share_c)
maindata['deltaInc' + str(year)] = (
(results_ref['u_nc' + str(year)] /
results_base['u_nc' + str(year)] - 1) * elast_nc +
(results_ref['eatr_nc' + str(year)] -
results_base['eatr_nc' + str(year)]) * selast_nc *
mne_share_nc)
maindata['MPKc' +
str(year)] = (results_ref['u_c' + str(year)] +
results_base['u_c' + str(year)]) / 2.0
maindata['MPKnc' +
str(year)] = (results_ref['u_nc' + str(year)] +
results_base['u_nc' + str(year)]) / 2.0
# Save the responses
self.investment_response = copy.deepcopy(maindata)
def __init__(self, btax_params, data=None):
# Store policy parameter objects
if isinstance(btax_params, pd.DataFrame):
self.btax_params = btax_params
else:
raise ValueError('btax_params must be DataFrame')
# Create Data object
if isinstance(data, Data):
self.data = data
else:
self.data = Data()
# Extract baseline forecast for earnings and action
self.cfc_data = copy.deepcopy(self.data.cfc_data)
self.cfc_data.set_index('Unnamed: 0', inplace=True)
# Generate earnings
self.create_earnings()
def __init__(self, btax_params, data=None):
# Store policy parameter object
if isinstance(btax_params, pd.DataFrame):
self.btax_params = btax_params
else:
raise ValueError('btax_params must be DataFrame')
# Create Data object
if data is None:
self.data = Data()
elif isinstance(data, Data):
self.data = data
else:
raise ValueError('data must be a Data object')
# Extract baseline forecast for earnings and action
self.dmne_data = copy.deepcopy(self.data.dmne_data)
# Create affiliated CFC
self.cfc = CFC(self.btax_params)
# For initial creation, set includes_response to False
self.includes_response = False
def __init__(self,
btax_params,
asset_forecast,
data=None,
response=None,
eta=0.4,
corp=True):
# Create an associated Data object
if isinstance(data, Data):
self.data = data
else:
self.data = Data()
if isinstance(corp, bool):
self.corp = corp
else:
raise ValueError('corp must be True or False')
if isinstance(btax_params, pd.DataFrame):
self.btax_params = btax_params
else:
raise ValueError('btax_params must be DataFrame')
if response is not None:
if len(response) == 14:
self.response = response
else:
raise ValueError('Wrong response')
else:
self.response = np.zeros(14)
if corp:
self.delta = np.array(
self.data.econ_defaults['f_c']) * (1 + self.response)
else:
self.delta = np.array(
self.data.econ_defaults['f_nc']) * (1 + self.response)
if len(asset_forecast) == 14:
self.asset_forecast = asset_forecast
else:
raise ValueError('Wrong length for asset forecast')
if eta >= 0 and eta <= 1:
self.eta = eta
else:
raise ValueError('Value of eta inappropriate')
def run_btax_mini(self, yearlist):
"""
Runs the code to compute the user cost and EATR
for each asset type for each year in yearlist.
"""
basedata = copy.deepcopy(Data().assets_data())
for year in yearlist:
# Get calculations for each year
results_oneyear = self.calc_oneyear(year)
# Rename to include the year calculated
results_oneyear.rename(columns={
'uc_c': 'u_c' + str(year),
'uc_nc': 'u_nc' + str(year),
'eatr_c': 'eatr_c' + str(year),
'eatr_nc': 'eatr_nc' + str(year)
},
inplace=True)
# Merge year's results into combined DataFrame
basedata = basedata.merge(right=results_oneyear,
how='outer',
on='Asset')
basedata.drop(['assets_c', 'assets_nc'], axis=1, inplace=True)
return basedata
def _calc_repatriation_response(self, btax_params_base, btax_params_ref):
"""
Calculates the change in the repatriation rate of current CFC
after-tax profits. The parameter used is the semi-elasticity of repatriations
with respect to the tax penalty from repatriating. The response is
only used for repatriations from current profits, as repatriations
from accumulated profits are too complicated to model explicity
and not relevant following the 2017 tax act.
Note that although the set-up for this may seem odd, it is designed to
ensure that no policy change results in to repatriation response,
regardless of the semielasticity used. The appropriate value for the
semi-elasticity is -10.66536949, which is consistent with the
repatriation rate as of 2014.
"""
# Get foreign tax rate
ftax = Data().cfc_data.loc[0, 'taxrt']
# Get domestic tax rate
dtax_base = np.asarray(btax_params_base['tau_c'])
dtax_ref = np.asarray(btax_params_ref['tau_c'])
# Get foreign dividend inclusion rate for CFCs
divrt_base = np.asarray(btax_params_base['foreign_dividend_inclusion'])
divrt_ref = np.asarray(btax_params_ref['foreign_dividend_inclusion'])
penalty_base = np.maximum(dtax_base - ftax, 0.) * divrt_base
penalty_ref = np.maximum(dtax_ref - ftax, 0.) * divrt_ref
# Compute change in repatriation rate
reprate_ch1 = (penalty_ref -
penalty_base) * self.elasticities['reprate_inc']
reprate_ch = np.zeros(NUM_YEARS)
for i in range(NUM_YEARS):
if i + 2014 >= self.elasticities['first_year_response']:
reprate_ch[i] = reprate_ch1[i]
repat_response = pd.DataFrame({
'year': range(START_YEAR, END_YEAR + 1),
'reprate_e': reprate_ch,
'reprate_a': np.zeros(NUM_YEARS)
})
self.repatriation_response = repat_response
def calcIDAdjustment(corp, eta=0.4):
"""
Calculates the adjustment factors for the corporate and noncorporate
debt and interest.
eta: retirement rate of existing debt
"""
data = Data()
policy = Policy()
policy_params_df = policy.parameters_dataframe()
# Create Asset object
asset = Asset(policy_params_df, corp)
asset.calc_all()
# Get asset forecast
forecast = asset.get_forecast()
# Create Debt object
debt = Debt(policy_params_df, forecast, corp=corp)
debt.calc_all()
# Get unscaled interest incomes and expenses
intpaid_model = debt.int_expense[40:54]
intinc_model = debt.int_income[40:54]
muniinc_model = debt.muni_income[40:54]
if corp:
# Exclude anomalous results for 2007
paid_scale = (sum(intpaid[:7] / intpaid_model[:7]) +
sum(intpaid[8:] / intpaid_model[8:])) / 13.
inc_scale = (sum(taxint[:7] / intinc_model[:7]) +
sum(taxint[8:] / intinc_model[8:])) / 13.
muni_scale = (sum(ntaxint[:7] / muniinc_model[:7]) +
sum(ntaxint[8:] / muniinc_model[8:]))/ 13.
scales = [paid_scale, inc_scale, muni_scale]
else:
ID_irs = np.array(data.debt_data_noncorp['ID_Scorp'][40:54] +
data.debt_data_noncorp['ID_sp'][40:54] +
data.debt_data_noncorp['ID_partner'][40:54])
scales = sum(ID_irs / intpaid_model) / 14.
assets14 = forecast[0]
return (scales, assets14, intpaid_model, intinc_model, muniinc_model)
class Asset():
"""
Constructor for the Asset class.
This class includes several objects related to assets and depreciation:
For internal class use:
investment_history:
array of investment amounts
asset type (95) x year investment made (68)
capital_history:
array of asset amounts
asset type (95) x years in the budget window (NUM_YEARS)
system_history:
array of depreciation systems (GDS or ADS)
asset type (95) x year investment made (68)
method_history:
array of depreciation methods (DB, Economics, etc.)
asset type (95) x year investment made (68)
life_history:
array of tax lives (see LIVES list)
asset type (95) and year investment made (68) given system
bonus_history:
array of effective bonus depreciation rates
asset type (95) x year investment made (68)
ccr_data:
DataFrame of asset amounts and economic depreciation rates
asset type (95), 2017 only
capital_path:
DataFrame of asset information totals in the budget window
Parameters:
corp: True for corporate, False for noncorporate
btax_params: dict of business tax policy parameters
response: DataFrame of investment responses
"""
def __init__(self,
btax_params,
corp=True,
data=None,
response=None,
industry='ALL'):
# Create an associated Data object
if isinstance(data, Data):
self.data = data
else:
self.data = Data()
# Check inputs
if isinstance(corp, bool):
self.corp = corp
else:
raise ValueError('corp must be True or False')
if response is None or isinstance(response, pd.DataFrame):
self.response = response
else:
raise ValueError('response must be DataFrame or None')
if corp:
self.adjustments = {
'bonus': 0.60290131,
'sec179': 0.016687178,
'rescalar': self.data.rescale_corp
}
else:
self.adjustments = {
'bonus': 0.453683778,
'sec179': 0.17299506,
'rescalar': self.data.rescale_noncorp
}
if isinstance(btax_params, pd.DataFrame):
self.btax_params = btax_params
else:
raise ValueError('btax_params must be DataFrame')
self.industry = industry
def update_response(self, response):
"""
Updates the response DataFrame.
Note: The response is the investment response DataFrame, not a
Response object.
"""
assert isinstance(response, pd.DataFrame)
self.response = response
def build_inv_matrix(self):
"""
Builds investment array by asset type and by year made
"""
# Get historical investment for 1960-2014
if self.corp:
investment_dfg = copy.deepcopy(self.data.investment_corp)
else:
investment_dfg = copy.deepcopy(self.data.investment_noncorp)
investment_df = investment_dfg.loc[investment_dfg.industry ==
self.industry, :]
investment_df.drop(['industry'], axis=1, inplace=True)
investment_df.reset_index(drop=True, inplace=True)
# Extend investment using NGDP (growth factors from CBO forecast)
inv2014 = np.asarray(investment_df.loc[:, str(START_YEAR)])
for year in range(START_YEAR + 1, END_YEAR + 1):
gfact1 = (
self.data.gfactors.loc[year - START_YEAR + 1, 'fi_nonres'] /
self.data.gfactors.loc[1, 'fi_nonres'])
gfact2 = (self.data.gfactors.loc[year - START_YEAR + 1, 'fi_res'] /
self.data.gfactors.loc[1, 'fi_res'])
investment_df.loc[:, str(year)] = inv2014 * gfact1
# Use residential investment gfactor for residential inv
investment_df.loc[91:, str(year)] = inv2014[91:] * gfact2
# Update investment matrix to include investment responses
if self.response is not None:
if self.corp:
deltaIkey = 'deltaIc'
else:
deltaIkey = 'deltaInc'
for year in range(START_YEAR, END_YEAR + 1):
deltaI = np.asarray(self.response[deltaIkey + str(year)])
investment_df.loc[:, str(year)] = (
investment_df.loc[:, str(year)] * (1. + deltaI))
self.investment_history = investment_df
def build_deprLaw_matrices(self):
"""
Builds the arrays for tax depreciation laws
"""
def taxdep_final(depr_methods, depr_bonuses, depr_file):
"""
Constructs the DataFrame of information for tax depreciation.
Only relevant for years beginning with START_YEAR.
Returns a DataFrame with:
depreciation method,
tax depreciation life,
true depreciation rate,
bonus depreciation rate.
"""
taxdep = copy.deepcopy(self.data.taxdep_info_gross(depr_file))
system = np.asarray(taxdep['System'])
life = np.asarray(taxdep['L_gds'])
# Determine depreciation systems for each asset class (by GDS life)
for cl, method in depr_methods.items():
system[life == cl] = method
# Determine tax life
L_ads = np.asarray(taxdep['L_ads'])
Llist = np.asarray(taxdep['L_gds'])
Llist[system == 'ADS'] = L_ads[system == 'ADS']
Llist[system == 'None'] = 9e99
taxdep['L'] = Llist
# Determine depreciation method. Default is GDS method
method = np.asarray(taxdep['Method'])
for i in range(len(method)):
if system[i] == 'ADS':
method[i] = np.asarray(taxdep['ADS method'])[i]
elif system[i] == 'Economic':
method[i] = 'Economic'
elif system[i] == 'None':
method[i] = 'None'
elif system[i] == 'Expensing':
method[i] = 'Expensing'
elif system[i] != 'GDS':
asset1 = np.asarray(taxdep['Asset'])[i]
raise ValueError('Must specify depreciation system for ' +
asset1 + '. Cannot use ' + str(system[i]))
taxdep['Method'] = method
# Detemine bonus depreciation rate
bonus = np.zeros(len(taxdep))
for cl, cl_bonus in depr_bonuses.items():
bonus[life == cl] = cl_bonus
taxdep['bonus'] = bonus
taxdep.drop(['L_gds', 'L_ads', 'Class life'], axis=1, inplace=True)
return taxdep
def taxdep_preset():
"""
Constructs the DataFrame of information for tax depreciation.
Only relevant for years before START_YEAR.
Returns a DataFrame with:
depreciation method,
tax depreciation life,
true depreciation rate,
bonus depreciation rate.
"""
taxdep = copy.deepcopy(self.data.taxdep_info_gross('pre2017'))
taxdep['L'] = taxdep['L_gds']
life = np.asarray(taxdep['L_gds'])
bonus = np.zeros(len(life))
for y in [3, 5, 7, 10, 15, 20, 25, 27.5, 39]:
s = "bonus{}".format(y if y != 27.5 else 27)
bonus[life == y] = self.data.bonus_data[s][year -
HISTORY_START]
taxdep['bonus'] = bonus
taxdep.drop(['L_gds', 'L_ads', 'Class life'], axis=1, inplace=True)
return taxdep
def get_btax_params_oneyear(btax_params, year):
"""
Extracts tax depreciation parameters and
calls the functions to build the tax depreciation
DataFrames.
"""
if year >= START_YEAR:
year = min(year, END_YEAR)
iyr = year - START_YEAR
depr_methods = {}
depr_bonuses = {}
for y in [3, 5, 7, 10, 15, 20, 25, 27.5, 39]:
s = "depr_{}yr_".format(y if y != 27.5 else 275)
depr_methods[y] = btax_params[s + 'method'][iyr]
depr_bonuses[y] = btax_params[s + 'bonus'][iyr]
depr_file = btax_params.loc[year - START_YEAR, 'depr_file']
taxdep = taxdep_final(depr_methods, depr_bonuses, depr_file)
else:
taxdep = taxdep_preset()
return taxdep
"""
Create arrays and store depreciation rules in them
"""
length1 = END_YEAR - HISTORY_START + 1
method_history = [[]] * length1
life_history = np.zeros((95, length1))
bonus_history = np.zeros((95, length1))
for year in range(HISTORY_START, END_YEAR + 1):
iyr = year - HISTORY_START
params_oneyear = get_btax_params_oneyear(self.btax_params, year)
method_history[iyr] = params_oneyear['Method']
life_history[:, iyr] = params_oneyear['L']
bonus_history[:, iyr] = params_oneyear['bonus']
self.method_history = method_history
self.life_history = life_history
self.bonus_history = bonus_history
def calcDep_oneyear(self, year):
"""
Calculates total depreciation deductions taken in the year.
"""
def depreciationDeduction(year_investment, year_deduction, method, L,
delta, bonus):
"""
Computes the nominal depreciation deduction taken on any
unit investment in any year with any depreciation method and life.
Parameters:
year_investment: year the investment is made
year_deduction: year the CCR deduction is taken
method: method of CCR (DB 200%, DB 150%, SL, Expensing, None)
L: class life for DB or SL depreciation (MACRS)
delta: economic depreciation rate
bonus: bonus depreciation rate
"""
assert method in [
'DB 200%', 'DB 150%', 'SL', 'Expensing', 'Economic', 'None'
]
# No depreciation
if method == 'None':
deduction = 0
# Expensing
elif method == 'Expensing':
if year_deduction == year_investment:
deduction = 1.0
else:
deduction = 0
# Economic depreciation
elif method == 'Economic':
yded = year_deduction
pi_temp = (self.data.investmentGfactors_data['pce'][yded + 1] /
self.data.investmentGfactors_data['pce'][yded])
if pi_temp == np.exp(delta):
annual_change = 1.0
else:
if year_deduction == year_investment:
annual_change = ((
(pi_temp * np.exp(delta / 2))**0.5 - 1) /
(np.log(pi_temp - delta)))
else:
annual_change = ((pi_temp * np.exp(delta) - 1) /
(np.log(pi_temp) - delta))
if year_deduction < year_investment:
sval = 0
deduction = 0
elif year_deduction == year_investment:
sval = 1.0
deduction = (bonus +
(1 - bonus) * delta * sval * annual_change)
else:
sval = (
np.exp(-delta * (year_deduction - year_investment)) *
self.data.investmentGfactors_data['pce']
[year_deduction] / 2.0 /
(self.data.investmentGfactors_data['pce']
[year_investment] + self.data.
investmentGfactors_data['pce'][year_investment + 1]))
deduction = (1 - bonus) * delta * sval * annual_change
else:
if method == 'DB 200%':
N = 2
elif method == 'DB 150%':
N = 1.5
elif method == 'SL':
N = 1
# DB or SL depreciation, half-year convention
t0 = year_investment + 0.5
t1 = t0 + L * (1 - 1 / N)
s1 = year_deduction
s2 = s1 + 1
if year_deduction < year_investment:
deduction = 0
elif year_deduction > year_investment + L:
deduction = 0
elif year_deduction == year_investment:
deduction = (bonus + (1 - bonus) *
(1 - np.exp(-N / L * 0.5)))
elif s2 <= t1:
deduction = ((1 - bonus) *
(np.exp(-N / L *
(s1 - t0)) - np.exp(-N / L *
(s2 - t0))))
elif s1 >= t1 and s1 <= t0 + L and s2 > t0 + L:
deduction = ((1 - bonus) * (N / L * np.exp(1 - N) *
(s2 - s1) * 0.5))
elif s1 >= t1 and s2 <= t0 + L:
deduction = ((1 - bonus) * (N / L * np.exp(1 - N) *
(s2 - s1)))
elif s1 < t1 and s2 > t1:
deduction = ((1 - bonus) *
(np.exp(-N / L *
(s1 - t0)) - np.exp(-N / L *
(t1 - t0)) +
N / L * np.exp(1 - N) * (s2 - t1)))
return deduction
"""
Calculate depreciation deductions for each year
"""
unitDep_arr = np.zeros((95, END_YEAR - HISTORY_START + 1))
depr_file = self.btax_params.loc[year - START_YEAR, 'depr_file']
delta = np.asarray(self.data.taxdep_info_gross(depr_file)['delta'])
for i in range(95):
# Iterate over asset types
for j in range(END_YEAR - HISTORY_START + 1):
# Iterate over investment years
bonus1 = min(
(self.bonus_history[i, j] * self.adjustments['bonus'] +
self.adjustments['sec179']), 1.0)
unitDep_arr[i, j] = depreciationDeduction(
j, year - HISTORY_START, self.method_history[j][i],
self.life_history[i, j], delta[i], bonus1)
inv_hist = copy.deepcopy(self.investment_history)
inv_hist.drop(['asset_code'], axis=1, inplace=True)
inv_hist2 = inv_hist.to_numpy()
Dep_arr = inv_hist2 * unitDep_arr
# Apply the haircut on undepreciated basis
iyr = year - START_YEAR
if year < START_YEAR:
# Use no haircut for years before calculator
hc_undep_year = 0
hc_undep = 0.
else:
if self.corp:
hc_undep_year = np.array(
self.btax_params['undepBasis_corp_hcyear'])[iyr]
hc_undep = np.array(
self.btax_params['undepBasis_corp_hc'])[iyr]
else:
hc_undep_year = np.array(
self.btax_params['undepBasis_noncorp_hcyear'])[iyr]
hc_undep = np.array(
self.btax_params['undepBasis_noncorp_hc'])[iyr]
if year >= hc_undep_year:
for j in range(END_YEAR - HISTORY_START + 1):
if j < hc_undep_year:
Dep_arr[:, j] = Dep_arr[:, j] * (1 - hc_undep)
# Asset types included in tax depreciation or not
other_assets = [68, 69, 70] # Software
other_assets.extend(range(71, 86)) # Add R&D categories
depr_assets = list(range(0, 68)) # Tangible assets
depr_assets.extend(range(86, 95)) # Add artistic originals
depr_assets.append(93) # Add residential
# Tax depreciation deduction
depded = Dep_arr[depr_assets, :].sum().sum()
# Other CCR deduction
otherded = Dep_arr[other_assets, :].sum().sum()
return [depded, otherded]
def calcDep_allyears(self):
"""
Calculates total depreciation deductions taken for all years
1960-2035.
"""
dep_deductions = np.zeros(END_YEAR + 1 - HISTORY_START)
for year in range(HISTORY_START, END_YEAR + 1):
dep_deductions[year -
HISTORY_START] = self.calcDep_oneyear(year)[0]
return dep_deductions
def calcDep_budget(self):
"""
Calculates total depreciation deductions taken for START_ to END_YEAR.
"""
dep_deductions = np.zeros(NUM_YEARS)
other_deductions = np.zeros(NUM_YEARS)
for iyr in range(0, NUM_YEARS):
year = iyr + START_YEAR
[depded, otherded] = self.calcDep_oneyear(year)
dep_deductions[iyr] = depded
other_deductions[iyr] = otherded
return dep_deductions
def build_capital_history(self):
"""
Builds capital history array using investment_history and capital_df
"""
# Get historical capital stock
if self.corp:
capital_dfg = copy.deepcopy(self.data.capital_corp)
else:
capital_dfg = copy.deepcopy(self.data.capital_noncorp)
capital_df1 = capital_dfg[capital_dfg.industry == self.industry]
capital_df1.drop(['industry'], axis=1, inplace=True)
capital_df1.reset_index(drop=True, inplace=True)
capital_df1.rename(columns={'asset_code': 'Code'}, inplace=True)
capital_df2 = capital_df1.merge(right=self.data.econ_depr_df(),
how='outer',
on='Code')
trueDep_df = copy.deepcopy(self.data.econ_depr_df())
pcelist = np.asarray(self.data.investmentGfactors_data['pce'])
for year in range(START_YEAR, END_YEAR + 1):
trueDep_df[str(
year)] = capital_df2[str(year)] * trueDep_df['delta']
capital_df2[str(year + 1)] = (
(capital_df2[str(year)] - trueDep_df[str(year)] +
self.investment_history[str(year)]) *
pcelist[year - HISTORY_START + 1] /
pcelist[year - HISTORY_START])
self.capital_history = capital_df2
self.trueDep = trueDep_df
def build_capital_path(self):
"""
Builds the DataFrame of asset amount, investment and depreciation
totals for each year in the budget window.
"""
# Sum across assets and put into new dataset
Kstock_total = np.zeros(NUM_YEARS)
trueDep_total = np.zeros(NUM_YEARS)
inv_total = np.zeros(NUM_YEARS)
Mdep_total = np.zeros(NUM_YEARS)
Oded_total = np.zeros(NUM_YEARS)
for year in range(START_YEAR, END_YEAR + 1):
iyr = year - START_YEAR
adjfactor = self.adjustments['rescalar'][iyr]
Kstock_total[iyr] = sum(
self.capital_history[str(year)]) * adjfactor
trueDep_total[iyr] = sum(self.trueDep[str(year)]) * adjfactor
inv_total[iyr] = sum(
self.investment_history[str(year)]) * adjfactor
[depded, otherded] = self.calcDep_oneyear(year)
Mdep_total[iyr] = depded * adjfactor
Oded_total[iyr] = otherded * adjfactor
cap_result = pd.DataFrame({
'year': range(START_YEAR, END_YEAR + 1),
'Kstock': Kstock_total,
'Investment': inv_total,
'trueDep': trueDep_total,
'taxDep': Mdep_total,
'otherCCR': Oded_total
})
self.capital_path = cap_result
def calc_all(self):
"""
Executes all calculations for Asset object.
"""
self.build_inv_matrix()
self.build_deprLaw_matrices()
self.build_capital_history()
self.build_capital_path()
return None
def get_forecast(self):
"""
Returns an array of the capital stock for [START_YEAR, END_YEAR]
"""
forecast = np.array(self.capital_path['Kstock'])
return forecast
def get_taxdep(self):
"""
Returns an array of tax depreciation deductions for
[START_YEAR, END_YEAR]
"""
taxdep = np.array(self.capital_path['taxDep'])
return taxdep
def get_investment(self):
"""
Returns an array of total investment for [START_YEAR, END_YEAR]
"""
inv1 = np.array(self.capital_path['Investment'])
return inv1
def get_truedep(self):
"""
Returns an array of true depreciation for [START_YEAR, END_YEAR]
"""
truedep = np.array(self.capital_path['trueDep'])
return truedep
def calc_oneyear(self, year):
"""
In the given year, calculates EATR and user cost of capital for each
asset type.
"""
# Check that year has acceptable value
assert year in range(2017, 2028)
# Extract economic parameters
[r_c, r_nc, r_d, pi, f_c, f_nc] = self.get_econ_params_oneyear(year)
# Extract tax depreciation information
Method = self.asset_c.method_history[year - 1960]
Life = self.asset_c.life_history[:, year - 1960]
Bonus = self.asset_c.bonus_history[:, year - 1960]
# Make tax rate dictionaries
tdict_c = self.make_tdict_c(year)
tdict_nc = self.make_tdict_nc(year)
# Create base DataFrame and get depreciation rates
asset_data = copy.deepcopy(Data().assets_data())
Delta = np.array(Data().econ_depr_df()['delta'])
# Get deductible fractions of interest paid
(fracded_c, fracded_nc) = self.calc_frac_ded(year)
# Get inventory method
inv_method = self.btax_params['inventory_method'][year - 2014]
assets = np.asarray(asset_data['Asset'])
uc_c = np.zeros(len(assets))
uc_nc = np.zeros(len(assets))
eatr_c = np.zeros(len(assets))
eatr_nc = np.zeros(len(assets))
for j in range(len(asset_data)):
uc_c[j] = self.calc_usercost(r_c, pi, Delta[j], Method[j], Life[j],
Bonus[j], f_c, r_d, fracded_c,
tdict_c, 50)
uc_nc[j] = self.calc_usercost(r_nc, pi, Delta[j], Method[j],
Life[j], Bonus[j], f_nc, r_d,
fracded_nc, tdict_nc, 50)
eatr_c[j] = self.calc_eatr(0.2,
r_c,
pi,
Delta[j],
Method[j],
Life[j],
Bonus[j],
f_c,
r_d,
fracded_c,
tdict_c,
length=50)
eatr_nc[j] = self.calc_eatr(0.2,
r_nc,
pi,
Delta[j],
Method[j],
Life[j],
Bonus[j],
f_nc,
r_d,
fracded_nc,
tdict_nc,
length=50)
# Special cost of capital calculations for inventories
uc_c[assets == 'Inventories'] = self.calc_rho_inv(
r_c, pi, inv_method, 0.5, tdict_c)
uc_nc[assets == 'Inventories'] = self.calc_rho_inv(
r_nc, pi, inv_method, 0.5, tdict_nc)
# EATR for inventories and land with no supernormal returns
eatr_c[assets == 'Inventories'] = (uc_c[assets == 'Inventories'] -
r_c) / uc_c[assets == 'Inventories']
eatr_nc[assets ==
'Inventories'] = (uc_nc[assets == 'Inventories'] -
r_nc) / uc_nc[assets == 'Inventories']
eatr_c[assets == 'Land'] = (uc_c[assets == 'Land'] -
r_c) / uc_c[assets == 'Land']
eatr_nc[assets == 'Land'] = (uc_nc[assets == 'Land'] -
r_nc) / uc_nc[assets == 'Land']
# Save the results to the main DataFrame
asset_data['uc_c'] = uc_c
asset_data['uc_nc'] = uc_nc
asset_data['eatr_c'] = eatr_c
asset_data['eatr_nc'] = eatr_nc
asset_data.drop(['assets_c', 'assets_nc'], axis=1, inplace=True)
return asset_data
def __init__(self, btax_params):
self.econ_params = copy.deepcopy(Data().econ_defaults)
self.btax_params = btax_params
self.asset_c = Asset(btax_params, corp=True)
self.asset_c.build_deprLaw_matrices()
class Corporation():
"""
Constructor for the Corporation class.
This contains both the real and tax information relevant to the
corporate income tax.
"""
def __init__(self, btax_params):
# Store policy parameter objects
if isinstance(btax_params, pd.DataFrame):
self.btax_params = btax_params
else:
raise ValueError('btax_params must be DataFrame')
# Create Data object
self.data = Data()
# Create Asset object and calculate
self.asset = Asset(self.btax_params, corp=True, data=self.data)
self.asset.calc_all()
# Create DomesticMNE object
self.dmne = DomesticMNE(self.btax_params)
self.dmne.calc_all()
# Create earnings forecast
self.create_earnings()
def create_debt(self):
"""
Creates the Debt object for the Corporation.
Note: create_asset must have already been called
"""
self.debt = Debt(self.btax_params,
self.asset.get_forecast(),
data=self.data,
corp=True)
self.debt.calc_all()
def create_earnings(self):
"""
Creates the initial forecast for earnings. Static only.
"""
# Grab forecasts of profit growth
earnings_forecast = np.asarray(self.data.gfactors['profit'])
gfacts = earnings_forecast[1:] / earnings_forecast[0]
# 2013 values for non-modeled revenues
taxitems = np.array(self.data.corp_tax2013['ALL'])
receipts = taxitems[4] * gfacts
rent_inc = taxitems[7] * gfacts
royalties = taxitems[8] * gfacts
capgains = (taxitems[9] + taxitems[10] + taxitems[11] -
taxitems[32]) * gfacts
domestic_divs = taxitems[12] * gfacts
other_recs = taxitems[14] * gfacts
# 2013 values for non-modeled deductions and credits
cogs = taxitems[16] * gfacts
execcomp = taxitems[17] * gfacts
wages = taxitems[18] * gfacts
repairs = taxitems[19] * gfacts
baddebt = taxitems[20] * gfacts
rent_paid = taxitems[21] * gfacts
statelocaltax = taxitems[22] * gfacts
charity = taxitems[24] * gfacts
amortization = taxitems[25] * gfacts
depletion = taxitems[27] * gfacts
advertising = taxitems[28] * gfacts
pensions = taxitems[29] * gfacts
benefits = taxitems[30] * gfacts
sec199_base = taxitems[31] * gfacts
other_ded = taxitems[33] * gfacts
gbc = taxitems[42] * gfacts
# Save unodeled tax items
self.revenues = pd.DataFrame({
'year': range(START_YEAR, END_YEAR + 1),
'receipts': receipts,
'rent': rent_inc,
'royalties': royalties,
'capgains': capgains,
'domestic_divs': domestic_divs,
'other': other_recs
})
self.deductions = pd.DataFrame({
'year': range(START_YEAR, END_YEAR + 1),
'cogs': cogs,
'execcomp': execcomp,
'wages': wages,
'repairs': repairs,
'baddebt': baddebt,
'rent': rent_paid,
'statelocaltax': statelocaltax,
'charity': charity,
'amortization': amortization,
'depletion': depletion,
'advertising': advertising,
'pensions': pensions,
'benefits': benefits,
'sec199share': sec199_base,
'other': other_ded
})
self.credits = pd.DataFrame({
'year': range(START_YEAR, END_YEAR + 1),
'gbc': gbc
})
def file_taxes(self):
"""
Creates the CorpTaxReturn object.
"""
self.taxreturn = CorpTaxReturn(self.btax_params,
self.revenues,
self.deductions,
self.credits,
dmne=self.dmne,
data=self.data,
assets=self.asset,
debts=self.debt)
self.taxreturn.calc_all()
def real_activity(self):
"""
Produces a DataFrame of the corporation's real activity.
Real measures are:
Capital stock
Investment
Depreciation (economic)
Net debt
Net interest paid
Earnings
Tax liability
Net income
Cash flow
"""
real_results = pd.DataFrame({'year': range(START_YEAR, END_YEAR + 1)})
real_results['Earnings'] = (
self.revenues['receipts'] + self.revenues['rent'] +
self.revenues['royalties'] + self.revenues['capgains'] +
self.revenues['domestic_divs'] + self.revenues['other'] -
self.deductions['cogs'] - self.deductions['execcomp'] -
self.deductions['wages'] - self.deductions['repairs'] -
self.deductions['baddebt'] - self.deductions['rent'] -
self.deductions['charity'] - self.deductions['depletion'] -
self.deductions['advertising'] - self.deductions['pensions'] -
self.deductions['benefits'] - self.deductions['other'] +
self.dmne.dmne_results['foreign_directinc'] +
self.dmne.dmne_results['foreign_indirectinc'])
real_results['Kstock'] = self.asset.get_forecast()
real_results['Inv'] = self.asset.get_investment()
real_results['Depr'] = self.asset.get_truedep()
real_results['Debt'] = self.debt.get_debt()
real_results['NIP'] = self.debt.get_nip()
real_results['Tax'] = self.taxreturn.get_tax()
real_results['NetInc'] = (real_results['Earnings'] -
real_results['Depr'] - real_results['NIP'] -
real_results['Tax'] -
self.dmne.dmne_results['foreign_tax'] -
self.deductions['statelocaltax'])
real_results['CashFlow'] = (real_results['Earnings'] -
real_results['Inv'] - real_results['Tax'] -
self.dmne.dmne_results['foreign_tax'] -
self.deductions['statelocaltax'])
self.real_results = real_results
def calc_static(self):
"""
Runs the static calculations.
"""
self.create_debt()
self.file_taxes()
self.real_activity()
def update_legal(self, responses):
"""
Updates the rescale_corp and rescale_noncorp associated with each
Data associated with each object.
"""
self.data.update_rescaling(responses.rescale_corp,
responses.rescale_noncorp)
self.asset.data.update_rescaling(responses.rescale_corp,
responses.rescale_noncorp)
def update_investment(self, responses):
"""
Updates the Asset object to include investment response.
"""
# First, save the capital stock by asset type and year (for earnings)
self.old_capital_history = copy.deepcopy(self.asset.capital_history)
self.asset.update_response(responses.investment_response)
self.asset.calc_all()
def update_repatriation(self, responses):
"""
Updates the DomesticMNE object to include the repatriation response.
Also updates profits to reflect this response.
"""
# First, save current foreign earnings
self.dmne.update_profits(responses.repatriation_response,
responses.shifting_response)
self.dmne.calc_all()
def update_earnings(self, responses):
"""
Recalculates earnings using the old capital stock by asset type, the
new capital stock by asset type (based on the investment response),
and the marginal product of capital.
"""
Kstock_base = copy.deepcopy(self.old_capital_history)
Kstock_ref = copy.deepcopy(self.asset.capital_history)
changeEarnings = np.zeros((95, NUM_YEARS))
for iyr in range(NUM_YEARS): # for each year
ystr = str(iyr + START_YEAR)
mpk = np.array(responses.investment_response['MPKc' + ystr])
for i in range(95): # by asset
changeEarnings[i, iyr] = (Kstock_ref[ystr][i] -
Kstock_base[ystr][i]) * mpk[i]
deltaE = np.zeros(NUM_YEARS)
for iyr in range(NUM_YEARS):
deltaE[iyr] = changeEarnings[:, iyr].sum()
# Update new earnings
self.revenues['receipts'] = self.revenues['receipts'] + deltaE
def update_debt(self, responses):
"""
Replaces the Debt object to use the new asset forecast and Data
"""
pctch_delta = np.array(responses.debt_response['pchDelta_corp'])
self.debt = Debt(self.btax_params,
self.asset.get_forecast(),
data=self.data,
response=pctch_delta,
corp=True)
self.debt.calc_all()
def apply_responses(self, responses):
"""
Updates Data, Asset, earnings, Debt and CorpTaxReturn to include
responses. Then calc_all() for each object.
"""
assert isinstance(responses, Response)
self.update_legal(responses)
self.update_investment(responses)
self.update_repatriation(responses)
self.update_earnings(responses)
self.update_debt(responses)
self.file_taxes()
self.real_activity()
def get_netinc(self):
"""
Returns an array of the corporation's net income (after-tax).
"""
netinc = np.array(self.real_results['NetInc'])
return netinc
def get_taxrev(self):
"""
Returns an array of the corporation's tax liability.
"""
taxrev = np.array(self.real_results['Tax'])
return taxrev
We need to produce the following objects:
adjfactors.csv
pass-through shares
"""
import copy
import numpy as np
import pandas as pd
import scipy.optimize
from biztax.data import Data
from biztax.asset import Asset
from biztax.debt import Debt
from biztax.policy import Policy
# Specify single Data object (for convenience)
data1 = Data()
"""
Section 1. Calculation of the adjustment parameters
"""
def calcAMTparams2():
"""
Calculates the adjustment factors for the AMT and PYMTC
"""
# Grab historical data
hist_data = copy.deepcopy(data1.historical_combined)
taxinc = np.array(hist_data['taxinc'])
amt = np.array(hist_data['amt'])
stock13 = 26.0
A13 = 4.196871
class Asset():
"""
Constructor for the Asset class.
This class includes several objects related to assets and depreciation:
For internal class use:
investment_history:
array of investment amounts
asset type (96) x year investment made (75)
capital_history:
array of asset amounts
asset type (96) x years in the budget window (14)
system_history:
array of depreciation systems (GDS or ADS)
asset type (96) x year investment made (75)
method_history:
array of depreciation methods (DB, Economics, etc.)
asset type (96) x year investment made (75)
life_history:
array of tax lives (see LIVES list)
asset type (96) and year investment made (75) given system
bonus_history:
array of effective bonus depreciation rates
asset type (96) x year investment made (75)
ccr_data:
DataFrame of asset amounts and economic depreciation rates
asset type (96), 2017 only
capital_path:
DataFrame of asset information totals in the budget window
Parameters:
corp: True for corporate, False for noncorporate
btax_params: dict of business tax policy parameters
response: DataFrame of investment responses
"""
def __init__(self, btax_params, corp=True, data=None, response=None):
# Create an associated Data object
if isinstance(data, Data):
self.data = data
else:
self.data = Data()
# Check inputs
if isinstance(corp, bool):
self.corp = corp
else:
raise ValueError('corp must be True or False')
if response is None or isinstance(response, pd.DataFrame):
self.response = response
else:
raise ValueError('response must be DataFrame or None')
if corp:
self.adjustments = {
'bonus': 0.60290131,
'sec179': 0.016687178,
'overall': self.data.adjfactor_dep_corp,
'rescalar': self.data.rescale_corp
}
else:
self.adjustments = {
'bonus': 0.453683778,
'sec179': 0.17299506,
'overall': self.data.adjfactor_dep_noncorp,
'rescalar': self.data.rescale_noncorp
}
if isinstance(btax_params, pd.DataFrame):
self.btax_params = btax_params
else:
raise ValueError('btax_params must be DataFrame')
def update_response(self, response):
"""
Updates the response DataFrame.
Note: The response is the investment response DataFrame, not a
Response object.
"""
assert isinstance(response, pd.DataFrame)
self.response = response
def get_ccr_data(self):
assets2017 = copy.deepcopy(self.data.assets_data())
ccrdata = assets2017.merge(right=self.data.econ_depr_df(),
how='outer',
on='Asset')
self.ccr_data = ccrdata
def build_inv_matrix(self):
"""
Builds investment array by asset type and by year made
"""
inv_mat1 = np.zeros((96, 75))
# Build historical portion
for j in range(57):
if self.corp:
inv_mat1[:, j] = (
self.data.investmentrate_data['i' + str(j + 1960)] *
self.data.investmentshare_data['c_share'][j])
else:
inv_mat1[:, j] = (
self.data.investmentrate_data['i' + str(j + 1960)] *
(1 - self.data.investmentshare_data['c_share'][j]))
# Extend investment using NGDP (growth factors from CBO forecast)
for j in range(57, 75):
inv_mat1[:, j] = (inv_mat1[:, 56] *
self.data.investmentGfactors_data['ngdp'][j] /
self.data.investmentGfactors_data['ngdp'][56])
# Investment forecast for 2017
if self.corp:
inv2017 = np.asarray(
self.ccr_data['assets_c'] *
(self.data.investmentGfactors_data['ngdp'][57] /
self.data.investmentGfactors_data['ngdp'][56] - 1 +
self.ccr_data['delta']))
else:
inv2017 = np.asarray(
self.ccr_data['assets_nc'] *
(self.data.investmentGfactors_data['ngdp'][57] /
self.data.investmentGfactors_data['ngdp'][56] - 1 +
self.ccr_data['delta']))
# Rescale to match B-Tax data for 2017
inv_mat2 = np.zeros((96, 75))
l1 = list(range(96))
l1.remove(32) # exclude land
for j in range(75):
for i in l1:
inv_mat2[i, j] = inv_mat1[i, j] * inv2017[i] / inv_mat1[i, 57]
# Update investment matrix to include investment responses
inv_mat3 = copy.deepcopy(inv_mat2)
if self.response is not None:
if self.corp:
deltaIkey = 'deltaIc'
else:
deltaIkey = 'deltaInc'
for i in range(96):
for j in range(57, 68):
deltaI = self.response[deltaIkey +
str(j + 1960)].tolist()[i]
inv_mat3[i, j] = inv_mat2[i, j] * (1 + deltaI)
self.investment_history = inv_mat3
def build_deprLaw_matrices(self):
"""
Builds the arrays for tax depreciation laws
"""
def taxdep_final(depr_methods, depr_bonuses):
"""
Constructs the DataFrame of information for tax depreciation.
Only relevant for years beginning with 2014.
Returns a DataFrame with:
depreciation method,
tax depreciation life,
true depreciation rate,
bonus depreciation rate.
"""
taxdep = copy.deepcopy(self.data.taxdep_info_gross())
system = np.empty(len(taxdep), dtype='S10')
class_life = np.asarray(taxdep['GDS Class Life'])
# Determine depreciation systems for each asset type
for cl, method in depr_methods.items():
system[class_life == cl] = method
# Determine tax life
L_ads = np.asarray(taxdep['L_ads'])
Llist = np.asarray(taxdep['L_gds'])
Llist[system == 'ADS'] = L_ads[system == 'ADS']
Llist[system == 'None'] = 100
taxdep['L'] = Llist
# Determine depreciation method. Default is GDS method
method = np.asarray(taxdep['Method'])
method[system == 'ADS'] = 'SL'
method[system == 'Economic'] = 'Economic'
method[system == 'None'] = 'None'
taxdep['Method'] = method
# Detemine bonus depreciation rate
bonus = np.zeros(len(taxdep))
for cl, cl_bonus in depr_bonuses.items():
bonus[class_life == cl] = cl_bonus
taxdep['bonus'] = bonus
taxdep.drop(['L_gds', 'L_ads', 'GDS Class Life'],
axis=1,
inplace=True)
return taxdep
def taxdep_preset(year):
"""
Constructs the DataFrame of information for tax depreciation.
Only relevant for years before 2014.
Returns a DataFrame with:
depreciation method,
tax depreciation life,
true depreciation rate,
bonus depreciation rate.
"""
taxdep = copy.deepcopy(self.data.taxdep_info_gross())
taxdep['L'] = taxdep['L_gds']
class_life = np.asarray(taxdep['GDS Class Life'])
bonus = np.zeros(len(class_life))
for y in [3, 5, 7, 10, 15, 20, 25, 27.5, 39]:
s = "bonus{}".format(y if y != 27.5 else 27)
bonus[class_life == y] = self.data.bonus_data[s][year - 1960]
taxdep['bonus'] = bonus
taxdep.drop(['L_gds', 'L_ads', 'GDS Class Life'],
axis=1,
inplace=True)
return taxdep
def get_btax_params_oneyear(btax_params, year):
"""
Extracts tax depreciation parameters and
calls the functions to build the tax depreciation
DataFrames.
"""
if year >= 2014:
year = min(year, 2027)
depr_methods = {}
depr_bonuses = {}
for y in [3, 5, 7, 10, 15, 20, 25, 27.5, 39]:
s = "depr_{}yr_".format(y if y != 27.5 else 275)
depr_methods[y] = btax_params[s + 'method'][year - 2014]
depr_bonuses[y] = btax_params[s + 'bonus'][year - 2014]
taxdep = taxdep_final(depr_methods, depr_bonuses)
else:
taxdep = taxdep_preset(year)
return taxdep
"""
Create arrays and store depreciation rules in them
"""
method_history = [[]] * 75
life_history = np.zeros((96, 75))
bonus_history = np.zeros((96, 75))
for year in range(1960, 2035):
params_oneyear = get_btax_params_oneyear(self.btax_params, year)
method_history[year - 1960] = params_oneyear['Method']
life_history[:, year - 1960] = params_oneyear['L']
bonus_history[:, year - 1960] = params_oneyear['bonus']
self.method_history = method_history
self.life_history = life_history
self.bonus_history = bonus_history
def calcDep_oneyear(self, year):
"""
Calculates total depreciation deductions taken in the year.
"""
def depreciationDeduction(year_investment, year_deduction, method, L,
delta, bonus):
"""
Computes the nominal depreciation deduction taken on any
unit investment in any year with any depreciation method and life.
Parameters:
year_investment: year the investment is made
year_deduction: year the CCR deduction is taken
method: method of CCR (DB 200%, DB 150%, SL, Expensing, None)
L: class life for DB or SL depreciation (MACRS)
delta: economic depreciation rate
bonus: bonus depreciation rate
"""
assert method in [
'DB 200%', 'DB 150%', 'SL', 'Expensing', 'Economic', 'None'
]
# No depreciation
if method == 'None':
deduction = 0
# Expensing
elif method == 'Expensing':
if year_deduction == year_investment:
deduction = 1.0
else:
deduction = 0
# Economic depreciation
elif method == 'Economic':
pi_temp = (
self.data.investmentGfactors_data['pce'][year_deduction +
1] /
self.data.investmentGfactors_data['pce'][year_deduction])
if pi_temp == np.exp(delta):
annual_change = 1.0
else:
if year_deduction == year_investment:
annual_change = ((
(pi_temp * np.exp(delta / 2))**0.5 - 1) /
(np.log(pi_temp - delta)))
else:
annual_change = ((pi_temp * np.exp(delta) - 1) /
(np.log(pi_temp) - delta))
if year_deduction < year_investment:
sval = 0
deduction = 0
elif year_deduction == year_investment:
sval = 1.0
deduction = bonus + (1 -
bonus) * delta * sval * annual_change
else:
sval = (
np.exp(-delta * (year_deduction - year_investment)) *
self.data.investmentGfactors_data['pce']
[year_deduction] / 2.0 /
(self.data.investmentGfactors_data['pce']
[year_investment] + self.data.
investmentGfactors_data['pce'][year_investment + 1]))
deduction = (1 - bonus) * delta * sval * annual_change
else:
if method == 'DB 200%':
N = 2
elif method == 'DB 150%':
N = 1.5
elif method == 'SL':
N = 1
# DB or SL depreciation, half-year convention
t0 = year_investment + 0.5
t1 = t0 + L * (1 - 1 / N)
s1 = year_deduction
s2 = s1 + 1
if year_deduction < year_investment:
deduction = 0
elif year_deduction > year_investment + L:
deduction = 0
elif year_deduction == year_investment:
deduction = bonus + (1 - bonus) * (1 -
np.exp(-N / L * 0.5))
elif s2 <= t1:
deduction = ((1 - bonus) *
(np.exp(-N / L *
(s1 - t0)) - np.exp(-N / L *
(s2 - t0))))
elif s1 >= t1 and s1 <= t0 + L and s2 > t0 + L:
deduction = (1 - bonus) * (N / L * np.exp(1 - N) *
(s2 - s1) * 0.5)
elif s1 >= t1 and s2 <= t0 + L:
deduction = (1 - bonus) * (N / L * np.exp(1 - N) *
(s2 - s1))
elif s1 < t1 and s2 > t1:
deduction = ((1 - bonus) *
(np.exp(-N / L *
(s1 - t0)) - np.exp(-N / L *
(t1 - t0)) +
N / L * np.exp(1 - N) * (s2 - t1)))
return deduction
"""
Calculate depreciation deductions for each year
"""
unitDep_arr = np.zeros((96, 75))
for i in range(96):
# Iterate over asset types
for j in range(75):
# Iterate over investment years
bonus1 = min(
self.bonus_history[i, j] * self.adjustments['bonus'] +
self.adjustments['sec179'], 1.0)
unitDep_arr[i, j] = depreciationDeduction(
j, year - 1960, self.method_history[j][i],
self.life_history[i, j], self.ccr_data['delta'][i], bonus1)
Dep_arr = self.investment_history * unitDep_arr
# Apply the haircut on undepreciated basis
if self.corp:
hc_undep_year = np.array(
self.btax_params['undepBasis_corp_hcyear'])[year - 2014]
hc_undep = np.array(self.btax_params['undepBasis_corp_hc'])[year -
2014]
else:
hc_undep_year = np.array(
self.btax_params['undepBasis_noncorp_hcyear'])[year - 2014]
hc_undep = np.array(
self.btax_params['undepBasis_noncorp_hc'])[year - 2014]
if year >= hc_undep_year:
for j in range(75):
if j < hc_undep_year:
Dep_arr[:, j] = Dep_arr[:, j] * (1 - hc_undep)
# Calculate the total deduction
total_depded = Dep_arr.sum().sum()
return total_depded
def calcDep_allyears(self):
"""
Calculates total depreciation deductions taken for all years
1960-2035.
"""
dep_deductions = np.zeros(75)
for year in range(1960, 2035):
dep_deductions[year - 1960] = self.calcDep_oneyear(year)
return dep_deductions
def calcDep_budget(self):
"""
Calculates total depreciation deductions taken for 2014 - 2027.
"""
dep_deductions = np.zeros(14)
for year in range(2014, 2028):
dep_deductions[year - 2014] = self.calcDep_oneyear(year)
return dep_deductions
def build_capital_history(self):
"""
Builds capital history array using investment_history and ccr_data
"""
Kstock = np.zeros((96, 15))
trueDep = np.zeros((96, 14))
pcelist = np.asarray(self.data.investmentGfactors_data['pce'])
deltalist = np.asarray(self.ccr_data['delta'])
for i in range(96):
# Starting by assigning 2017 data from B-Tax
if self.corp:
Kstock[i, 3] = np.asarray(self.ccr_data['assets_c'])[i]
else:
Kstock[i, 3] = np.asarray(self.ccr_data['assets_nc'])[i]
# Using 2017 asset totals, apply retroactively
for j in [56, 55, 54]:
Kstock[i, j - 54] = (
(Kstock[i, j - 53] * pcelist[j] / pcelist[j + 1] -
self.investment_history[i, j]) / (1 - deltalist[i]))
trueDep[i, j - 54] = Kstock[i, j - 54] * deltalist[i]
# Using 2017 asset totals, apply to future
for j in range(57, 68):
trueDep[i, j - 54] = Kstock[i, j - 54] * deltalist[i]
Kstock[i, j - 53] = (
(Kstock[i, j - 54] + self.investment_history[i, j] -
trueDep[i, j - 54]) * pcelist[j + 1] / pcelist[j])
self.capital_history = Kstock
self.trueDep = trueDep
def build_capital_path(self):
"""
Builds the DataFrame of asset amount, investment and depreciation
totals for each year in the budget window.
"""
# Sum across assets and put into new dataset
Kstock_total = np.zeros(14)
fixedK_total = np.zeros(14)
trueDep_total = np.zeros(14)
inv_total = np.zeros(14)
fixedInv_total = np.zeros(14)
Mdep_total = np.zeros(14)
for j in range(14):
adjfactor = self.adjustments['overall'] * self.adjustments[
'rescalar'][j]
Kstock_total[j] = sum(self.capital_history[:, j]) * adjfactor
fixedK_total[j] = (
(sum(self.capital_history[:, j]) -
self.capital_history[31, j] - self.capital_history[32, j]) *
adjfactor)
trueDep_total[j] = sum(self.trueDep[:, j]) * adjfactor
inv_total[j] = (sum(self.investment_history[:, j + 54]) *
adjfactor)
fixedInv_total[j] = ((sum(self.investment_history[:, j + 54]) -
self.investment_history[31, j + 54]) *
adjfactor)
Mdep_total[j] = self.calcDep_oneyear(j + 2014) * adjfactor
cap_result = pd.DataFrame({
'year': range(2014, 2028),
'Kstock': Kstock_total,
'Investment': inv_total,
'FixedInv': fixedInv_total,
'trueDep': trueDep_total,
'taxDep': Mdep_total,
'FixedK': fixedK_total
})
self.capital_path = cap_result
def calc_all(self):
"""
Executes all calculations for Asset object.
"""
self.get_ccr_data()
self.build_inv_matrix()
self.build_deprLaw_matrices()
self.build_capital_history()
self.build_capital_path()
return None
def get_forecast(self):
"""
Returns an array of the capital stock for 2014-2027
"""
forecast = np.array(self.capital_path['Kstock'])
return forecast
def get_taxdep(self):
"""
Returns an array of tax depreciation deductions for 2014-2027
"""
taxdep = np.array(self.capital_path['taxDep'])
return taxdep
def get_investment(self):
"""
Returns an array of total investment for 2014-2027
"""
inv1 = np.array(self.capital_path['Investment'])
return inv1
def get_truedep(self):
"""
Returns an array of true depreciation for 2014-2027
"""
truedep = np.array(self.capital_path['trueDep'])
return truedep
def calc_oneyear(self, year):
"""
In the given year, calculates EATR and user cost of capital for each
asset type.
"""
# Check that year has acceptable value
assert year in range(2017, END_YEAR + 1)
# Extract economic parameters
[r_c, r_nc, r_d, pi, f_c, f_nc] = self.get_econ_params_oneyear(year)
# Extract tax depreciation information
iyr = year - 1960
Method = self.asset_c.method_history[iyr]
Life = self.asset_c.life_history[:, iyr]
Bonus = self.asset_c.bonus_history[:, iyr]
# Make tax rate dictionaries
tdict_c = self.make_tdict_c(year)
tdict_nc = self.make_tdict_nc(year)
# Create base DataFrame and get depreciation rates
asset_data = copy.deepcopy(Data().taxdep_info_gross('pre2017'))
asset_data.drop(['L_gds', 'L_ads', 'Method'], axis=1, inplace=True)
Delta = np.array(asset_data['delta'])
# Get deductible fractions of interest paid
(fracded_c, fracded_nc) = self.calc_frac_ded(year)
# Get inventory method
assets = np.asarray(asset_data['Asset'])
uc_c = np.zeros(len(assets))
uc_nc = np.zeros(len(assets))
eatr_c = np.zeros(len(assets))
eatr_nc = np.zeros(len(assets))
for j in range(len(asset_data)):
uc_c[j] = self.calc_usercost(r_c, pi, Delta[j], Method[j], Life[j],
Bonus[j], f_c, r_d, fracded_c,
tdict_c, 50)
uc_nc[j] = self.calc_usercost(r_nc, pi, Delta[j], Method[j],
Life[j], Bonus[j], f_nc, r_d,
fracded_nc, tdict_nc, 50)
eatr_c[j] = self.calc_eatr(0.2,
r_c,
pi,
Delta[j],
Method[j],
Life[j],
Bonus[j],
f_c,
r_d,
fracded_c,
tdict_c,
length=50)
eatr_nc[j] = self.calc_eatr(0.2,
r_nc,
pi,
Delta[j],
Method[j],
Life[j],
Bonus[j],
f_nc,
r_d,
fracded_nc,
tdict_nc,
length=50)
# Save the results to the main DataFrame
asset_data['uc_c'] = uc_c
asset_data['uc_nc'] = uc_nc
asset_data['eatr_c'] = eatr_c
asset_data['eatr_nc'] = eatr_nc
return asset_data
class PassThrough():
"""
Constructor for the PassThrough class.
This contains the calculation of pass-through business income. All other
components of pass-through taxation occur through Tax-Calculator.
For now, a PassThrough object contains 6 different business entities:
sole proprietorship, positive net income
sole proprietorship, negative net income
S corporation, positive net income
S corporation, negative net income
partnership, positive net income
partnership, negative net income
Therefore, the process for modeling the pass-through sector evolves in
two stages. The first, like for the Corporation class, produces a single
Asset object, Debt object and earnings for the pass-through sector. Once
these are calculated, they are split between each of the 6 entities. The
results from these will later be used by the Investor class to distribute
the changes in business income to individuals in Tax-Calculator.
The following functions apply to the sector as a whole:
create_asset()
create_earnings()
create_debt()
real_activity()
"""
def __init__(self, btax_params):
# Store policy parameter objects
if isinstance(btax_params, pd.DataFrame):
self.btax_params = btax_params
else:
raise ValueError('btax_params must be DataFrame')
# Create Data object
self.data = Data()
# Create Asset object and calculate
self.asset = Asset(self.btax_params, corp=False, data=self.data)
self.asset.calc_all()
# Create earnings forecast
self.create_earnings()
def create_earnings(self):
"""
Creates the initial forecast for earnings. Static only.
"""
# Get initial EBITDA for 2014, for those in net income positions
sp_posinc2014 = np.array(self.data.sp_data['netinc'])[-1]
part_posinc2014 = np.array(self.data.partner_data['netinc_total'])[-1]
scorp_posinc2014 = np.array(self.data.Scorp_data['netinc_total'])[-1]
# Get initial EBITDA for 2014, for those in net loss positions
sp_neginc2014 = -np.array(self.data.sp_data['netloss'])[-1]
part_neginc2014 = -np.array(self.data.partner_data['netloss_total'])[-1]
scorp_neginc2014 = -np.array(self.data.Scorp_data['netloss_total'])[-1]
# Get growth factor for noncorporate business income and apply it
gfact_propinc = np.array(self.data.gfactors['propinc_nonfarm'])[1:]
sp_posinc = sp_posinc2014 / gfact_propinc[0] * gfact_propinc
part_posinc = part_posinc2014 / gfact_propinc[0] * gfact_propinc
scorp_posinc = scorp_posinc2014 / gfact_propinc[0] * gfact_propinc
sp_neginc = sp_neginc2014 / gfact_propinc[0] * gfact_propinc
part_neginc = part_neginc2014 / gfact_propinc[0] * gfact_propinc
scorp_neginc = scorp_neginc2014 / gfact_propinc[0] * gfact_propinc
# Aggregate and save EBITDAs
total_inc = (sp_posinc + sp_neginc + part_posinc + part_neginc +
scorp_posinc + scorp_neginc)
earnings_result = pd.DataFrame({'year': range(2014,2028),
'total': total_inc,
'SchC_pos': sp_posinc,
'SchC_neg': sp_neginc,
'partner_pos': part_posinc,
'partner_neg': part_neginc,
'Scorp_pos': scorp_posinc,
'Scorp_neg': scorp_neginc})
self.earnings = earnings_result
def create_debt(self):
"""
Creates the Debt object for the pass-through sector.
Note: create_asset must have already been called
"""
self.debt = Debt(self.btax_params, self.asset.get_forecast(),
data=self.data, corp=False)
self.debt.calc_all()
def real_activity(self):
"""
Produces a DataFrame of the pass-through sector's real activity.
Real measures are:
Capital stock
Investment
Depreciation (economic)
Debt
Interest paid
Earnings
Net income
Cash flow
Note that unlike for a corporation, the final real activity measures
(net income and cash flow) are pre-tax, as these would be passed to
units in Tax-Calculator.
"""
real_results = pd.DataFrame({'year': range(2014,2028),
'Earnings': self.earnings['total']})
real_results['Kstock'] = self.asset.get_forecast()
real_results['Inv'] = self.asset.get_investment()
real_results['Depr'] = self.asset.get_truedep()
real_results['Debt'] = self.debt.get_debt()
real_results['NIP'] = self.debt.get_nip()
real_results['NetInc'] = real_results['Earnings'] - real_results['Depr'] - real_results['NIP']
real_results['CashFlow'] = real_results['Earnings'] - real_results['Inv']
self.real_results = real_results
def calc_schC(self):
"""
Calculates net income for sole proprietorships
"""
SchC_results = pd.DataFrame({'year': range(2014,2028)})
# Update earnings
SchC_results['ebitda_pos'] = self.earnings['SchC_pos']
SchC_results['ebitda_neg'] = self.earnings['SchC_neg']
# Update tax depreciation
SchC_results['dep_pos'] = (self.asset.get_taxdep() * self.data.depshare_sp_posinc)
SchC_results['dep_neg'] = (self.asset.get_taxdep() * self.data.depshare_sp_neginc)
# Update interest deduction
SchC_results['intded_pos'] = (self.debt.get_nid() * self.data.intshare_sp_posinc)
SchC_results['intded_neg'] = (self.debt.get_nid() * self.data.intshare_sp_neginc)
# Update business net income
SchC_results['netinc_pos'] = SchC_results['ebitda_pos'] - SchC_results['dep_pos'] - SchC_results['intded_pos']
SchC_results['netinc_neg'] = SchC_results['ebitda_neg'] - SchC_results['dep_neg'] - SchC_results['intded_neg']
self.SchC_results = SchC_results
def calc_partner(self):
"""
Calculates net income for partnerships
"""
partner_results = pd.DataFrame({'year': range(2014,2028)})
# Update earnings
partner_results['ebitda_pos'] = self.earnings['partner_pos']
partner_results['ebitda_neg'] = self.earnings['partner_neg']
# Update tax depreciation
partner_results['dep_pos'] = (self.asset.get_taxdep() * self.data.depshare_partner_posinc)
partner_results['dep_neg'] = (self.asset.get_taxdep() * self.data.depshare_partner_neginc)
# Update interest deduction
partner_results['intded_pos'] = (self.debt.get_nid() * self.data.intshare_partner_posinc)
partner_results['intded_neg'] = (self.debt.get_nid() * self.data.intshare_partner_neginc)
# Update business net income
partner_results['netinc_pos'] = partner_results['ebitda_pos'] - partner_results['dep_pos'] - partner_results['intded_pos']
partner_results['netinc_neg'] = partner_results['ebitda_neg'] - partner_results['dep_neg'] - partner_results['intded_neg']
self.partner_results = partner_results
def calc_Scorp(self):
"""
Calculates net income for S corporations
"""
Scorp_results = pd.DataFrame({'year': range(2014,2028)})
# Update earnings
Scorp_results['ebitda_pos'] = self.earnings['Scorp_pos']
Scorp_results['ebitda_neg'] = self.earnings['Scorp_neg']
# Update tax depreciation
Scorp_results['dep_pos'] = (self.asset.get_taxdep() * self.data.depshare_scorp_posinc)
Scorp_results['dep_neg'] = (self.asset.get_taxdep() * self.data.depshare_scorp_neginc)
# Update interest deduction
Scorp_results['intded_pos'] = (self.debt.get_nid() * self.data.intshare_scorp_posinc)
Scorp_results['intded_neg'] = (self.debt.get_nid() * self.data.intshare_scorp_neginc)
# Update business net income
Scorp_results['netinc_pos'] = Scorp_results['ebitda_pos'] - Scorp_results['dep_pos'] - Scorp_results['intded_pos']
Scorp_results['netinc_neg'] = Scorp_results['ebitda_neg'] - Scorp_results['dep_neg'] - Scorp_results['intded_neg']
self.Scorp_results = Scorp_results
def calc_netinc(self):
"""
Runs all calculations for each entity and saves the net income results.
"""
self.calc_schC()
self.calc_partner()
self.calc_Scorp()
netinc_results = pd.DataFrame({'year': range(2014,2028)})
netinc_results['SchC_pos'] = self.SchC_results['netinc_pos']
netinc_results['SchC_neg'] = self.SchC_results['netinc_neg']
netinc_results['partner_pos'] = self.SchC_results['netinc_pos']
netinc_results['partner_neg'] = self.SchC_results['netinc_neg']
netinc_results['Scorp_pos'] = self.SchC_results['netinc_pos']
netinc_results['Scorp_neg'] = self.SchC_results['netinc_neg']
self.netinc_results = netinc_results
def calc_static(self):
"""
Runs the static calculations
"""
self.create_debt()
self.real_activity()
self.calc_netinc()
def update_legal(self, responses):
"""
Updates the rescale_corp and rescale_noncorp associated with each
Data associated with each object.
"""
self.data.update_rescaling(responses.rescale_corp, responses.rescale_noncorp)
self.asset.data.update_rescaling(responses.rescale_corp, responses.rescale_noncorp)
def update_investment(self, responses):
"""
Updates the Asset object to include investment response.
"""
# First, save the capital stock by asset type and year (for earnings)
self.old_capital_history = copy.deepcopy(self.asset.capital_history)
self.asset.update_response(responses.investment_response)
self.asset.calc_all()
def update_earnings(self, responses):
"""
Recalculates earnings using the old capital stock by asset type, the
new capital stock by asset type (based on the investment response),
and the marginal product of capital.
"""
Kstock_base = copy.deepcopy(self.old_capital_history)
Kstock_ref = copy.deepcopy(self.asset.capital_history)
deltaK = Kstock_ref - Kstock_base
changeEarnings = np.zeros((96, 14))
for j in range(14): # for each year
mpk = np.array(responses.investment_response['MPKnc' + str(j + 2014)])
for i in range(96): # by asset
changeEarnings[i,j] = deltaK[i,j] * mpk[i] * self.data.adjfactor_dep_noncorp
deltaE = np.zeros(14)
for j in range(14):
deltaE[j] = changeEarnings[:, j].sum()
earnings_old = np.array(self.earnings['total'])
ebitda_chgfactor = (earnings_old + deltaE) * self.data.rescale_noncorp / earnings_old
keylist = list(self.earnings)
for key in keylist:
self.earnings[key] = self.earnings[key] * ebitda_chgfactor
def update_debt(self, responses):
"""
Replaces the Debt object to use the new asset forecast and Data
"""
pctch_delta = np.array(responses.debt_response['pchDelta_corp'])
self.debt = Debt(self.btax_params, self.asset.get_forecast(),
data=self.data, response=pctch_delta, corp=False)
self.debt.calc_all()
def apply_responses(self, responses):
"""
Updates Data, Asset, earnings, and Debt to include
responses.
"""
assert isinstance(responses, Response)
self.update_legal(responses)
self.update_investment(responses)
self.update_earnings(responses)
self.update_debt(responses)
self.real_activity()
self.calc_netinc()
def __init__(self,
btax_params,
revenues,
deductions,
credit,
dmne=None,
data=None,
assets=None,
debts=None):
# Create an associated Data object
if isinstance(data, Data):
self.data = data
else:
self.data = Data()
if isinstance(btax_params, pd.DataFrame):
self.btax_params = btax_params
else:
raise ValueError('btax_params must be DataFrame')
if isinstance(revenues, pd.DataFrame):
self.revenues = copy.deepcopy(revenues)
else:
raise ValueError('revenues must be in DataFrame')
if isinstance(deductions, pd.DataFrame):
self.deductions = copy.deepcopy(deductions)
else:
raise ValueError('deductions must be in DataFrame')
if isinstance(credit, pd.DataFrame):
self.credits = copy.deepcopy(credit)
else:
raise ValueError('credits must be in DataFrame')
if dmne is None:
# Note: Don't do this in general
self.dmne = DomesticMNE(self.btax_params)
self.dmne.calc_all()
elif isinstance(dmne, DomesticMNE):
self.dmne = dmne
else:
raise ValueError('dmne must be a DomesticMNE object')
if assets is not None:
if isinstance(assets, Asset):
self.assets = assets
else:
raise ValueError('assets must be Asset object')
else:
self.assets = Asset(btax_params)
self.assets.calc_all()
if debts is not None:
if isinstance(debts, Debt):
self.debts = debts
else:
raise ValueError('debts must be Debt object')
else:
assets_forecast = self.assets.get_forecast()
self.debts = Debt(btax_params, assets_forecast)
self.debts.calc_all()
# Prepare unmodeled components of tax return
self.revenues['capgains'] = (
self.revenues['capgains'] *
(1. - self.btax_params['capgains_corp_hc']))
self.revenues['domestic_divs'] = (
self.revenues['domestic_divs'] *
self.btax_params['domestic_dividend_inclusion'])
self.revenues['total'] = (self.revenues['receipts'] +
self.revenues['rent'] +
self.revenues['royalties'] +
self.revenues['capgains'] +
self.revenues['domestic_divs'] +
self.revenues['other'] +
self.dmne.dmne_results['foreign_taxinc'])
self.deductions['charity'] = (self.deductions['charity'] *
(1. - self.btax_params['charity_hc']))
self.deductions['statelocaltax'] = (
self.deductions['statelocaltax'] *
(1. - self.btax_params['statelocaltax_hc']))
self.deductions['total'] = (
self.deductions['cogs'] + self.deductions['execcomp'] +
self.deductions['wages'] + self.deductions['repairs'] +
self.deductions['baddebt'] + self.deductions['rent'] +
self.deductions['statelocaltax'] + self.deductions['charity'] +
self.deductions['amortization'] + self.deductions['depletion'] +
self.deductions['advertising'] + self.deductions['pensions'] +
self.deductions['benefits'] + self.deductions['other'])
combined = pd.DataFrame({
'year':
range(START_YEAR, END_YEAR + 1),
'ebitda': (self.revenues['total'] - self.deductions['total'])
})
# Add tax depreciation
combined['taxDep'] = self.assets.get_taxdep()
self.combined_return = combined