def __init__(self, y, x, regimes,
w=None, robust=None, gwk=None, sig2n_k=True,
nonspat_diag=True, spat_diag=False, moran=False, white_test=False,
vm=False, constant_regi='many', cols2regi='all',
regime_err_sep=True, cores=None,
name_y=None, name_x=None, name_regimes=None,
name_w=None, name_gwk=None, name_ds=None):
n = USER.check_arrays(y, x)
USER.check_y(y, n)
USER.check_weights(w, y)
USER.check_robust(robust, gwk)
USER.check_spat_diag(spat_diag, w)
self.name_x_r = USER.set_name_x(name_x, x)
self.constant_regi = constant_regi
self.cols2regi = cols2regi
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
self.name_ds = USER.set_name_ds(name_ds)
self.name_y = USER.set_name_y(name_y)
self.name_regimes = USER.set_name_ds(name_regimes)
self.n = n
cols2regi = REGI.check_cols2regi(
constant_regi, cols2regi, x, add_cons=False)
self.regimes_set = REGI._get_regimes_set(regimes)
self.regimes = regimes
USER.check_regimes(self.regimes_set, self.n, x.shape[1])
if regime_err_sep == True and robust == 'hac':
set_warn(
self, "Error by regimes is incompatible with HAC estimation. Hence, error by regimes has been disabled for this model.")
regime_err_sep = False
self.regime_err_sep = regime_err_sep
if regime_err_sep == True and set(cols2regi) == set([True]) and constant_regi == 'many':
self.y = y
name_x = USER.set_name_x(name_x, x)
regi_ids = dict((r, list(np.where(np.array(regimes) == r)[0]))
for r in self.regimes_set)
self._ols_regimes_multi(x, w, regi_ids, cores,
gwk, sig2n_k, robust, nonspat_diag, spat_diag, vm, name_x, moran, white_test)
else:
name_x = USER.set_name_x(name_x, x, constant=True)
x, self.name_x = REGI.Regimes_Frame.__init__(self, x,
regimes, constant_regi, cols2regi, name_x)
BaseOLS.__init__(self, y=y, x=x, robust=robust,
gwk=gwk, sig2n_k=sig2n_k)
if regime_err_sep == True and robust == None:
y2, x2 = REGI._get_weighted_var(
regimes, self.regimes_set, sig2n_k, self.u, y, x)
ols2 = BaseOLS(y=y2, x=x2, sig2n_k=sig2n_k)
RegressionProps_basic(self, betas=ols2.betas, vm=ols2.vm)
self.title = "ORDINARY LEAST SQUARES - REGIMES (Group-wise heteroskedasticity)"
nonspat_diag = None
set_warn(
self, "Residuals treated as homoskedastic for the purpose of diagnostics.")
else:
self.title = "ORDINARY LEAST SQUARES - REGIMES"
self.robust = USER.set_robust(robust)
self.chow = REGI.Chow(self)
SUMMARY.OLS(reg=self, vm=vm, w=w, nonspat_diag=nonspat_diag,
spat_diag=spat_diag, moran=moran, white_test=white_test, regimes=True)
def __init__(self, y, x, regimes,
w=None, robust=None, gwk=None, sig2n_k=True,
nonspat_diag=True, spat_diag=False, moran=False, white_test=False,
vm=False, constant_regi='many', cols2regi='all',
regime_err_sep=True, cores=False,
name_y=None, name_x=None, name_regimes=None,
name_w=None, name_gwk=None, name_ds=None):
n = USER.check_arrays(y, x)
USER.check_y(y, n)
USER.check_weights(w, y)
USER.check_robust(robust, gwk)
USER.check_spat_diag(spat_diag, w)
self.name_x_r = USER.set_name_x(name_x, x)
self.constant_regi = constant_regi
self.cols2regi = cols2regi
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
self.name_ds = USER.set_name_ds(name_ds)
self.name_y = USER.set_name_y(name_y)
self.name_regimes = USER.set_name_ds(name_regimes)
self.n = n
cols2regi = REGI.check_cols2regi(
constant_regi, cols2regi, x, add_cons=False)
self.regimes_set = REGI._get_regimes_set(regimes)
self.regimes = regimes
USER.check_regimes(self.regimes_set, self.n, x.shape[1])
if regime_err_sep == True and robust == 'hac':
set_warn(
self, "Error by regimes is incompatible with HAC estimation. Hence, error by regimes has been disabled for this model.")
regime_err_sep = False
self.regime_err_sep = regime_err_sep
if regime_err_sep == True and set(cols2regi) == set([True]) and constant_regi == 'many':
self.y = y
name_x = USER.set_name_x(name_x, x)
regi_ids = dict(
(r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set)
self._ols_regimes_multi(x, w, regi_ids, cores,
gwk, sig2n_k, robust, nonspat_diag, spat_diag, vm, name_x, moran, white_test)
else:
name_x = USER.set_name_x(name_x, x, constant=True)
x, self.name_x = REGI.Regimes_Frame.__init__(self, x,
regimes, constant_regi, cols2regi, name_x)
BaseOLS.__init__(
self, y=y, x=x, robust=robust, gwk=gwk, sig2n_k=sig2n_k)
if regime_err_sep == True and robust == None:
y2, x2 = REGI._get_weighted_var(
regimes, self.regimes_set, sig2n_k, self.u, y, x)
ols2 = BaseOLS(y=y2, x=x2, sig2n_k=sig2n_k)
RegressionProps_basic(self, betas=ols2.betas, vm=ols2.vm)
self.title = "ORDINARY LEAST SQUARES - REGIMES (Group-wise heteroskedasticity)"
nonspat_diag = None
set_warn(
self, "Residuals treated as homoskedastic for the purpose of diagnostics.")
else:
self.title = "ORDINARY LEAST SQUARES - REGIMES"
self.robust = USER.set_robust(robust)
self.chow = REGI.Chow(self)
SUMMARY.OLS(reg=self, vm=vm, w=w, nonspat_diag=nonspat_diag,
spat_diag=spat_diag, moran=moran, white_test=white_test, regimes=True)
def __init__(self, y, x, regimes,\
w=None, robust=None, gwk=None, sig2n_k=True,\
nonspat_diag=True, spat_diag=False, moran=False,\
vm=False, constant_regi='many', cols2regi='all',\
regime_err_sep=False, cores=None,\
name_y=None, name_x=None, name_regimes=None,\
name_w=None, name_gwk=None, name_ds=None):
n = USER.check_arrays(y, x)
USER.check_y(y, n)
USER.check_weights(w, y)
USER.check_robust(robust, gwk)
USER.check_spat_diag(spat_diag, w)
self.name_x_r = USER.set_name_x(name_x, x)
self.constant_regi = constant_regi
self.cols2regi = cols2regi
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
self.name_ds = USER.set_name_ds(name_ds)
self.name_y = USER.set_name_y(name_y)
self.name_regimes = USER.set_name_ds(name_regimes)
self.n = n
if regime_err_sep == True:
name_x = USER.set_name_x(name_x, x)
self.y = y
if cols2regi == 'all':
cols2regi = [True] * (x.shape[1])
self.regimes_set = list(set(regimes))
self.regimes_set.sort()
if w:
w_i,regi_ids,warn = REGI.w_regimes(w, regimes, self.regimes_set, transform=True, get_ids=True, min_n=len(self.cols2regi)+1)
set_warn(self,warn)
else:
regi_ids = dict((r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set)
w_i = None
if set(cols2regi) == set([True]):
self._ols_regimes_multi(x, w_i, regi_ids, cores,\
gwk, sig2n_k, robust, nonspat_diag, spat_diag, vm, name_x, moran)
else:
raise Exception, "All coefficients must vary accross regimes if regime_err_sep = True."
else:
name_x = USER.set_name_x(name_x, x,constant=True)
x, self.name_x = REGI.Regimes_Frame.__init__(self, x,\
regimes, constant_regi, cols2regi, name_x)
BaseOLS.__init__(self, y=y, x=x, robust=robust, gwk=gwk, \
sig2n_k=sig2n_k)
self.title = "ORDINARY LEAST SQUARES - REGIMES"
self.robust = USER.set_robust(robust)
self.chow = REGI.Chow(self)
SUMMARY.OLS(reg=self, vm=vm, w=w, nonspat_diag=nonspat_diag,\
spat_diag=spat_diag, moran=moran, regimes=True)
def _run_stp1(y,x,eq_ids,r):
y_r = y[eq_ids[r]]
x_r = x[eq_ids[r]]
x_constant = USER.check_constant(x_r)
model = BaseOLS(y_r, x_constant)
#model.logll = diagnostics.log_likelihood(model)
return model
def _work(y,x,regi_ids,r,robust,sig2n_k,name_ds,name_y,name_x,name_w,name_regimes):
y_r = y[regi_ids[r]]
x_r = x[regi_ids[r]]
x_constant = USER.check_constant(x_r)
if robust == 'hac':
robust = None
model = BaseOLS(y_r, x_constant, robust=robust, sig2n_k=sig2n_k)
model.title = "ORDINARY LEAST SQUARES ESTIMATION - REGIME %s" %r
model.robust = USER.set_robust(robust)
model.name_ds = name_ds
model.name_y = '%s_%s'%(str(r), name_y)
model.name_x = ['%s_%s'%(str(r), i) for i in name_x]
model.name_w = name_w
model.name_regimes = name_regimes
return model
def _work(y,x,regi_ids,r,robust,sig2n_k,name_ds,name_y,name_x,name_w,name_regimes):
y_r = y[regi_ids[r]]
x_r = x[regi_ids[r]]
x_constant = USER.check_constant(x_r)
if robust == 'hac':
robust = None
model = BaseOLS(y_r, x_constant, robust=robust, sig2n_k=sig2n_k)
model.title = "ORDINARY LEAST SQUARES ESTIMATION - REGIME %s" %r
model.robust = USER.set_robust(robust)
model.name_ds = name_ds
model.name_y = '%s_%s'%(str(r), name_y)
model.name_x = ['%s_%s'%(str(r), i) for i in name_x]
model.name_w = name_w
model.name_regimes = name_regimes
return model
def _run_stp1(y, x, yend, q, eq_ids, r, sig2n_k, w, w_lags, lag_q):
y_r = y[eq_ids[r]]
x_r = x[eq_ids[r]]
yend_r = None
if yend != None:
yend_r = yend[eq_ids[r]]
q_r = q[eq_ids[r]]
if w_lags:
yend_r, q_r = set_endog_sparse(y_r, x_r, w, yend_r, q_r, w_lags, lag_q)
x_constant = USER.check_constant(x_r)
if yend != None:
model = BaseTSLS(y_r, x_constant, yend_r, q_r, sig2n_k=sig2n_k)
model.zhat = np.dot(model.h, np.dot(model.hthi, model.htz))
else:
model = BaseOLS(y_r, x_constant, sig2n_k=sig2n_k)
model.zhat = model.x
model.z = model.x
return model
