def calc_other(self, df):
'''
Calculates variables that depend on z and rho
'''
dfs = {'c': df[df['tax_treat'] == 'corporate'].copy(),
'nc': df[df['tax_treat'] == 'non-corporate'].copy()}
# separate into corp and non-corp dataframe here
for t in self.__p.entity_list:
for f in self.__p.financing_list:
dfs[t]['ucc_' + str(f)] = eq_ucc(
dfs[t]['rho_' + str(f)], dfs[t]['delta'])
dfs[t]['metr_' + str(f)] = eq_metr(
dfs[t]['rho_' + str(f)], self.__p.r_prime[t][f],
self.__p.inflation_rate)
dfs[t]['mettr_' + str(f)] = eq_mettr(
dfs[t]['rho_' + str(f)], self.__p.s[t][f])
dfs[t]['tax_wedge_' + str(f)] = eq_tax_wedge(
dfs[t]['rho_' + str(f)], self.__p.s[t][f])
dfs[t]['eatr_' + str(f)] = eq_eatr(
dfs[t]['rho_' + str(f)], dfs[t]['metr_' + str(f)],
self.__p.profit_rate, self.__p.u[t])
df = pd.concat(dfs, ignore_index=True, copy=True)
return df
def test_eq_tax_wedge(rho, s, expected_val):
test_val = cf.eq_tax_wedge(rho, s)
assert(np.allclose(test_val, expected_val))
def test_eq_tax_wedge(rho, s, expected_val):
test_val = cf.eq_tax_wedge(rho, s)
assert (np.allclose(test_val, expected_val))