def calcDepAdjustment(corp):
"""
Calculates the adjustment factor for assets, depreciation and investment
corp: indicator for whether corporate or noncorporate data
"""
# Create Asset object
policy = Policy()
asset1 = Asset(policy.parameters_dataframe(), corp)
asset1.calc_all()
# Get unscaled depreciation for all years
totalAnnualDepreciation = asset1.calcDep_allyears()
# Get IRS data
depreciation_data = copy.deepcopy(asset1.data.depreciationIRS_data)
depreciation_data['dep_model'] = totalAnnualDepreciation[40:54]
if corp:
depreciation_data['scale'] = (depreciation_data['dep_Ccorp'] /
depreciation_data['dep_model'])
else:
depreciation_data['scale'] = (
(depreciation_data['dep_Scorp'] + depreciation_data['dep_sp'] +
depreciation_data['dep_partner']) /
depreciation_data['dep_model'])
adj_factor = (sum(depreciation_data['scale']) /
len(depreciation_data['scale']))
# depreciation_data.to_csv('depr_data_' + str(corp) + '.csv')
return adj_factor
def calcIDAdjustment(corp, eta=0.4):
"""
Calculates the adjustment factors for the corporate and noncorporate
debt and interest.
eta: retirement rate of existing debt
"""
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 net interest deduction
NID_gross = debt.NID[38:54]
# Get net interest deduction from historical IRS data
if corp:
NID_irs = np.array(data1.debt_data_corp['NID_IRS'])[38:54]
else:
NID_irs = np.array(data1.debt_data_noncorp['ID_Scorp'][38:54] +
data1.debt_data_noncorp['ID_sp'][38:54] +
data1.debt_data_noncorp['ID_partner'][38:54])
NID_scale = sum(NID_irs / NID_gross) / 16.0 # 16 = 54 - 38
return NID_scale
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 __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 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)
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()
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
assets14 = forecast[0]
return (scales, assets14, intpaid_model, intinc_model, muniinc_model)
(scales_corp, ast_c_14, intpaid_model, intinc_model, muniinc_model) = calcIDAdjustment(True)
(scale_ncorp, ast_nc_14, _, _, _) = calcIDAdjustment(False)
print(scales_corp)
print(scale_ncorp)
newdebt = copy.deepcopy(debt2)
newdebt['L_c'] = debt2['L_c'] * scales_corp[0]
newdebt['L_nc'] = debt2['L_nc'] * scale_ncorp
newdebt['At_c'] = debt2['At_c'] * scales_corp[1]
newdebt['An_c'] = debt2['An_c'] * scales_corp[2]
newdebt.to_csv(OUTPUT_PATH + 'debt_history.csv')
pol1 = Policy()
# Create Asset object
asset = Asset(pol1.parameters_dataframe(), True)
asset.calc_all()
# Create Debt object
debt = Debt(pol1.parameters_dataframe(), asset.get_forecast(), corp=True)
debt.calc_all()
df1 = pd.DataFrame({'inc_mod': intinc_model * scales_corp[1],
'paid_mod': intpaid_model * scales_corp[0],
'muni_mod': muniinc_model * scales_corp[2],
'inc_irs': taxint, 'paid_irs': intpaid, 'muni_irs': ntaxint})
df1.to_csv('debt_fit.csv')