def _work(y, x, w, regi_ids, r, yend, q, robust, sig2n_k, name_ds, name_y, name_x, name_yend, name_q, name_w, name_regimes):
y_r = y[regi_ids[r]]
x_r = x[regi_ids[r]]
yend_r = yend[regi_ids[r]]
q_r = q[regi_ids[r]]
x_constant = USER.check_constant(x_r)
if robust == 'hac' or robust == 'ogmm':
robust2 = None
else:
robust2 = robust
model = BaseTSLS(y_r, x_constant, yend_r, q_r,
robust=robust2, sig2n_k=sig2n_k)
model.title = "TWO STAGE LEAST SQUARES ESTIMATION - REGIME %s" % r
if robust == 'ogmm':
_optimal_weight(model, sig2n_k, warn=False)
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_yend = ['%s_%s' % (str(r), i) for i in name_yend]
model.name_z = model.name_x + model.name_yend
model.name_q = ['%s_%s' % (str(r), i) for i in name_q]
model.name_h = model.name_x + model.name_q
model.name_w = name_w
model.name_regimes = name_regimes
if w:
w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
set_warn(model, warn)
model.w = w_r
return model
def __init__(self, y, x, yend=None, q=None,\
w=None, w_lags=1, lag_q=True,\
robust=None, gwk=None, sig2n_k=False,\
spat_diag=False,\
vm=False, name_y=None, name_x=None,\
name_yend=None, name_q=None,\
name_w=None, name_gwk=None, name_ds=None):
n = USER.check_arrays(x, yend, q)
USER.check_y(y, n)
USER.check_weights(w, y, w_required=True)
USER.check_robust(robust, gwk)
yend2, q2 = set_endog(y, x, w, yend, q, w_lags, lag_q)
x_constant = USER.check_constant(x)
BaseGM_Lag.__init__(self, y=y, x=x_constant, w=w.sparse, yend=yend2, q=q2,\
w_lags=w_lags, robust=robust, gwk=gwk,\
lag_q=lag_q, sig2n_k=sig2n_k)
self.predy_e, self.e_pred, warn = sp_att(w,self.y,self.predy,\
yend2[:,-1].reshape(self.n,1),self.betas[-1])
set_warn(self,warn)
self.title = "SPATIAL TWO STAGE LEAST SQUARES"
self.name_ds = USER.set_name_ds(name_ds)
self.name_y = USER.set_name_y(name_y)
self.name_x = USER.set_name_x(name_x, x)
self.name_yend = USER.set_name_yend(name_yend, yend)
self.name_yend.append(USER.set_name_yend_sp(self.name_y))
self.name_z = self.name_x + self.name_yend
self.name_q = USER.set_name_q(name_q, q)
self.name_q.extend(USER.set_name_q_sp(self.name_x, w_lags, self.name_q, lag_q))
self.name_h = USER.set_name_h(self.name_x, self.name_q)
self.robust = USER.set_robust(robust)
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
SUMMARY.GM_Lag(reg=self, w=w, vm=vm, spat_diag=spat_diag)
def _work(y, x, w, regi_ids, r, yend, q, robust, sig2n_k, name_ds, name_y,
name_x, name_yend, name_q, name_w, name_regimes):
y_r = y[regi_ids[r]]
x_r = x[regi_ids[r]]
yend_r = yend[regi_ids[r]]
q_r = q[regi_ids[r]]
x_constant = USER.check_constant(x_r)
if robust == 'hac' or robust == 'ogmm':
robust2 = None
else:
robust2 = robust
model = BaseTSLS(y_r,
x_constant,
yend_r,
q_r,
robust=robust2,
sig2n_k=sig2n_k)
model.title = "TWO STAGE LEAST SQUARES ESTIMATION - REGIME %s" % r
if robust == 'ogmm':
_optimal_weight(model, sig2n_k, warn=False)
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_yend = ['%s_%s' % (str(r), i) for i in name_yend]
model.name_z = model.name_x + model.name_yend
model.name_q = ['%s_%s' % (str(r), i) for i in name_q]
model.name_h = model.name_x + model.name_q
model.name_w = name_w
model.name_regimes = name_regimes
if w:
w_r, warn = REGI.w_regime(w, regi_ids[r], r, transform=True)
set_warn(model, warn)
model.w = w_r
return model
def __init__(self, y, x, yend, q,
w=None,
robust=None, gwk=None, sig2n_k=False,
spat_diag=False,
vm=False, name_y=None, name_x=None,
name_yend=None, name_q=None,
name_w=None, name_gwk=None, name_ds=None):
n = USER.check_arrays(y, x, yend, q)
USER.check_y(y, n)
USER.check_weights(w, y)
USER.check_robust(robust, gwk)
USER.check_spat_diag(spat_diag, w)
x_constant = USER.check_constant(x)
BaseTSLS.__init__(self, y=y, x=x_constant, yend=yend, q=q,
robust=robust, gwk=gwk, sig2n_k=sig2n_k)
self.title = "TWO STAGE LEAST SQUARES"
self.name_ds = USER.set_name_ds(name_ds)
self.name_y = USER.set_name_y(name_y)
self.name_x = USER.set_name_x(name_x, x)
self.name_yend = USER.set_name_yend(name_yend, yend)
self.name_z = self.name_x + self.name_yend
self.name_q = USER.set_name_q(name_q, q)
self.name_h = USER.set_name_h(self.name_x, self.name_q)
self.robust = USER.set_robust(robust)
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
SUMMARY.TSLS(reg=self, vm=vm, w=w, spat_diag=spat_diag)
def __init__(self, y, x, yend=None, q=None,\
w=None, w_lags=1, lag_q=True,\
robust=None, gwk=None, sig2n_k=False,\
spat_diag=False,\
vm=False, name_y=None, name_x=None,\
name_yend=None, name_q=None,\
name_w=None, name_gwk=None, name_ds=None):
n = USER.check_arrays(x, yend, q)
USER.check_y(y, n)
USER.check_weights(w, y, w_required=True)
USER.check_robust(robust, gwk)
yend2, q2 = set_endog(y, x, w, yend, q, w_lags, lag_q)
x_constant = USER.check_constant(x)
BaseGM_Lag.__init__(self, y=y, x=x_constant, w=w.sparse, yend=yend2, q=q2,\
w_lags=w_lags, robust=robust, gwk=gwk,\
lag_q=lag_q, sig2n_k=sig2n_k)
self.predy_e, self.e_pred, warn = sp_att(w,self.y,self.predy,\
yend2[:,-1].reshape(self.n,1),self.betas[-1])
set_warn(self, warn)
self.title = "SPATIAL TWO STAGE LEAST SQUARES"
self.name_ds = USER.set_name_ds(name_ds)
self.name_y = USER.set_name_y(name_y)
self.name_x = USER.set_name_x(name_x, x)
self.name_yend = USER.set_name_yend(name_yend, yend)
self.name_yend.append(USER.set_name_yend_sp(self.name_y))
self.name_z = self.name_x + self.name_yend
self.name_q = USER.set_name_q(name_q, q)
self.name_q.extend(
USER.set_name_q_sp(self.name_x, w_lags, self.name_q, lag_q))
self.name_h = USER.set_name_h(self.name_x, self.name_q)
self.robust = USER.set_robust(robust)
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
SUMMARY.GM_Lag(reg=self, w=w, vm=vm, spat_diag=spat_diag)
def _work(y,x,regi_ids,r,yend,q,w_r,w_lags,lag_q,robust,sig2n_k,name_ds,name_y,name_x,name_yend,name_q,name_w,name_regimes):
y_r = y[regi_ids[r]]
x_r = x[regi_ids[r]]
if yend != None:
yend_r = yend[regi_ids[r]]
else:
yend_r = yend
if q != None:
q_r = q[regi_ids[r]]
else:
q_r = q
yend_r, q_r = set_endog_sparse(y_r, x_r, w_r, yend_r, q_r, w_lags, lag_q)
x_constant = USER.check_constant(x_r)
if robust == 'hac':
robust = None
model = BaseTSLS(y_r, x_constant, yend_r, q_r, robust=robust, sig2n_k=sig2n_k)
model.title = "SPATIAL TWO STAGE 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_yend = ['%s_%s'%(str(r), i) for i in name_yend]
model.name_z = model.name_x + model.name_yend
model.name_q = ['%s_%s'%(str(r), i) for i in name_q]
model.name_h = model.name_x + model.name_q
model.name_w = name_w
model.name_regimes = name_regimes
return model
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, yend, q,
w=None,
robust=None, gwk=None, sig2n_k=False,
spat_diag=False,
vm=False, name_y=None, name_x=None,
name_yend=None, name_q=None,
name_w=None, name_gwk=None, name_ds=None):
n = USER.check_arrays(y, x, yend, q)
USER.check_y(y, n)
USER.check_weights(w, y)
USER.check_robust(robust, gwk)
USER.check_spat_diag(spat_diag, w)
x_constant = USER.check_constant(x)
BaseTSLS.__init__(self, y=y, x=x_constant, yend=yend, q=q,
robust=robust, gwk=gwk, sig2n_k=sig2n_k)
self.title = "TWO STAGE LEAST SQUARES"
self.name_ds = USER.set_name_ds(name_ds)
self.name_y = USER.set_name_y(name_y)
self.name_x = USER.set_name_x(name_x, x)
self.name_yend = USER.set_name_yend(name_yend, yend)
self.name_z = self.name_x + self.name_yend
self.name_q = USER.set_name_q(name_q, q)
self.name_h = USER.set_name_h(self.name_x, self.name_q)
self.robust = USER.set_robust(robust)
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
SUMMARY.TSLS(reg=self, vm=vm, w=w, spat_diag=spat_diag)
def __init__(self, y, x, yend, q, regimes,\
w=None, robust=None, gwk=None, sig2n_k=True,\
spat_diag=False, vm=False, constant_regi='many',\
cols2regi='all', regime_err_sep=False, name_y=None, name_x=None,\
cores=None, name_yend=None, name_q=None, name_regimes=None,\
name_w=None, name_gwk=None, name_ds=None, summ=True):
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.constant_regi = constant_regi
self.cols2regi = cols2regi
self.name_ds = USER.set_name_ds(name_ds)
self.name_regimes = USER.set_name_ds(name_regimes)
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
self.name_y = USER.set_name_y(name_y)
name_yend = USER.set_name_yend(name_yend, yend)
name_q = USER.set_name_q(name_q, q)
self.name_x_r = USER.set_name_x(name_x, x) + name_yend
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]+yend.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._tsls_regimes_multi(x, yend, q, w_i, regi_ids, cores,\
gwk, sig2n_k, robust, spat_diag, vm, name_x, name_yend, name_q)
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)
q, self.name_q = REGI.Regimes_Frame.__init__(self, q, \
regimes, constant_regi=None, cols2regi='all', names=name_q)
x, self.name_x = REGI.Regimes_Frame.__init__(self, x, \
regimes, constant_regi, cols2regi=cols2regi, names=name_x)
yend, self.name_yend = REGI.Regimes_Frame.__init__(self, yend, \
regimes, constant_regi=None, \
cols2regi=cols2regi, yend=True, names=name_yend)
BaseTSLS.__init__(self, y=y, x=x, yend=yend, q=q, \
robust=robust, gwk=gwk, sig2n_k=sig2n_k)
self.name_z = self.name_x + self.name_yend
self.name_h = USER.set_name_h(self.name_x, self.name_q)
self.chow = REGI.Chow(self)
self.robust = USER.set_robust(robust)
if summ:
self.title = "TWO STAGE LEAST SQUARES - REGIMES"
SUMMARY.TSLS(reg=self, vm=vm, w=w, spat_diag=spat_diag, regimes=True)
def _work(
y,
x,
regi_ids,
r,
yend,
q,
w_r,
w_lags,
lag_q,
robust,
sig2n_k,
name_ds,
name_y,
name_x,
name_yend,
name_q,
name_w,
name_regimes,
):
y_r = y[regi_ids[r]]
x_r = x[regi_ids[r]]
if yend != None:
yend_r = yend[regi_ids[r]]
else:
yend_r = yend
if q != None:
q_r = q[regi_ids[r]]
else:
q_r = q
yend_r, q_r = set_endog_sparse(y_r, x_r, w_r, yend_r, q_r, w_lags, lag_q)
x_constant = USER.check_constant(x_r)
if robust == "hac" or robust == "ogmm":
robust2 = None
else:
robust2 = robust
model = BaseTSLS(y_r, x_constant, yend_r, q_r, robust=robust2, sig2n_k=sig2n_k)
model.title = "SPATIAL TWO STAGE LEAST SQUARES ESTIMATION - REGIME %s" % r
if robust == "ogmm":
_optimal_weight(model, sig2n_k, warn=False)
model.rho = model.betas[-1]
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_yend = ["%s_%s" % (str(r), i) for i in name_yend]
model.name_z = model.name_x + model.name_yend
model.name_q = ["%s_%s" % (str(r), i) for i in name_q]
model.name_h = model.name_x + model.name_q
model.name_w = name_w
model.name_regimes = name_regimes
return model
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 _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 __init__(self,
y,
x,
w=None,
robust=None,
gwk=None,
sig2n_k=True,
nonspat_diag=True,
spat_diag=False,
moran=False,
white_test=False,
vm=False,
name_y=None,
name_x=None,
name_w=None,
name_gwk=None,
name_ds=None):
n = USER.check_arrays(y, x)
y = USER.check_y(y, n)
USER.check_weights(w, y)
USER.check_robust(robust, gwk)
USER.check_spat_diag(spat_diag, w)
x_constant = USER.check_constant(x)
BaseOLS.__init__(self,
y=y,
x=x_constant,
robust=robust,
gwk=gwk,
sig2n_k=sig2n_k)
self.title = "ORDINARY LEAST SQUARES"
self.name_ds = USER.set_name_ds(name_ds)
self.name_y = USER.set_name_y(name_y)
self.name_x = USER.set_name_x(name_x, x)
self.robust = USER.set_robust(robust)
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
SUMMARY.OLS(reg=self,
vm=vm,
w=w,
nonspat_diag=nonspat_diag,
spat_diag=spat_diag,
moran=moran,
white_test=white_test)
def __init__(self, y, x,\
w=None,\
robust=None, gwk=None, sig2n_k=True,\
nonspat_diag=True, spat_diag=False, moran=False,\
vm=False, name_y=None, name_x=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)
x_constant = USER.check_constant(x)
BaseOLS.__init__(self, y=y, x=x_constant, robust=robust,\
gwk=gwk, sig2n_k=sig2n_k)
self.title = "ORDINARY LEAST SQUARES"
self.name_ds = USER.set_name_ds(name_ds)
self.name_y = USER.set_name_y(name_y)
self.name_x = USER.set_name_x(name_x, x)
self.robust = USER.set_robust(robust)
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
SUMMARY.OLS(reg=self, vm=vm, w=w, nonspat_diag=nonspat_diag,\
spat_diag=spat_diag, moran=moran)
def __init__(self,
y,
x,
yend,
q,
regimes,
w=None,
robust=None,
gwk=None,
sig2n_k=True,
spat_diag=False,
vm=False,
constant_regi='many',
cols2regi='all',
regime_err_sep=True,
name_y=None,
name_x=None,
cores=False,
name_yend=None,
name_q=None,
name_regimes=None,
name_w=None,
name_gwk=None,
name_ds=None,
summ=True):
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.constant_regi = constant_regi
self.cols2regi = cols2regi
self.name_ds = USER.set_name_ds(name_ds)
self.name_regimes = USER.set_name_ds(name_regimes)
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
self.name_y = USER.set_name_y(name_y)
name_yend = USER.set_name_yend(name_yend, yend)
name_q = USER.set_name_q(name_q, q)
self.name_x_r = USER.set_name_x(name_x, x) + name_yend
self.n = n
cols2regi = REGI.check_cols2regi(constant_regi,
cols2regi,
x,
yend=yend,
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 for 2SLS models. Hence, the 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':
name_x = USER.set_name_x(name_x, x)
self.y = y
regi_ids = dict((r, list(np.where(np.array(regimes) == r)[0]))
for r in self.regimes_set)
self._tsls_regimes_multi(x, yend, q, w, regi_ids, cores, gwk,
sig2n_k, robust, spat_diag, vm, name_x,
name_yend, name_q)
else:
name_x = USER.set_name_x(name_x, x, constant=True)
q, self.name_q = REGI.Regimes_Frame.__init__(self,
q,
regimes,
constant_regi=None,
cols2regi='all',
names=name_q)
x, self.name_x = REGI.Regimes_Frame.__init__(self,
x,
regimes,
constant_regi,
cols2regi=cols2regi,
names=name_x)
yend, self.name_yend = REGI.Regimes_Frame.__init__(
self,
yend,
regimes,
constant_regi=None,
cols2regi=cols2regi,
yend=True,
names=name_yend)
if regime_err_sep == True and robust == None:
robust = 'white'
BaseTSLS.__init__(self,
y=y,
x=x,
yend=yend,
q=q,
robust=robust,
gwk=gwk,
sig2n_k=sig2n_k)
self.title = "TWO STAGE LEAST SQUARES - REGIMES"
if robust == 'ogmm':
_optimal_weight(self, sig2n_k)
self.name_z = self.name_x + self.name_yend
self.name_h = USER.set_name_h(self.name_x, self.name_q)
self.chow = REGI.Chow(self)
self.robust = USER.set_robust(robust)
if summ:
SUMMARY.TSLS(reg=self,
vm=vm,
w=w,
spat_diag=spat_diag,
regimes=True)
def __init__(self, y, x, yend, q, regimes,
w=None, robust=None, gwk=None, sig2n_k=True,
spat_diag=False, vm=False, constant_regi='many',
cols2regi='all', regime_err_sep=True, name_y=None, name_x=None,
cores=None, name_yend=None, name_q=None, name_regimes=None,
name_w=None, name_gwk=None, name_ds=None, summ=True):
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.constant_regi = constant_regi
self.cols2regi = cols2regi
self.name_ds = USER.set_name_ds(name_ds)
self.name_regimes = USER.set_name_ds(name_regimes)
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
self.name_y = USER.set_name_y(name_y)
name_yend = USER.set_name_yend(name_yend, yend)
name_q = USER.set_name_q(name_q, q)
self.name_x_r = USER.set_name_x(name_x, x) + name_yend
self.n = n
cols2regi = REGI.check_cols2regi(
constant_regi, cols2regi, x, yend=yend, 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 for 2SLS models. Hence, the 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':
name_x = USER.set_name_x(name_x, x)
self.y = y
regi_ids = dict((r, list(np.where(np.array(regimes) == r)[0]))
for r in self.regimes_set)
self._tsls_regimes_multi(x, yend, q, w, regi_ids, cores,
gwk, sig2n_k, robust, spat_diag, vm, name_x, name_yend, name_q)
else:
name_x = USER.set_name_x(name_x, x, constant=True)
q, self.name_q = REGI.Regimes_Frame.__init__(self, q,
regimes, constant_regi=None, cols2regi='all', names=name_q)
x, self.name_x = REGI.Regimes_Frame.__init__(self, x,
regimes, constant_regi, cols2regi=cols2regi, names=name_x)
yend, self.name_yend = REGI.Regimes_Frame.__init__(self, yend,
regimes, constant_regi=None,
cols2regi=cols2regi, yend=True, names=name_yend)
if regime_err_sep == True and robust == None:
robust = 'white'
BaseTSLS.__init__(self, y=y, x=x, yend=yend, q=q,
robust=robust, gwk=gwk, sig2n_k=sig2n_k)
self.title = "TWO STAGE LEAST SQUARES - REGIMES"
if robust == 'ogmm':
_optimal_weight(self, sig2n_k)
self.name_z = self.name_x + self.name_yend
self.name_h = USER.set_name_h(self.name_x, self.name_q)
self.chow = REGI.Chow(self)
self.robust = USER.set_robust(robust)
if summ:
SUMMARY.TSLS(reg=self, vm=vm, w=w,
spat_diag=spat_diag, regimes=True)
def __init__(self, y, x, yend, q, regimes,\
w=None, robust=None, gwk=None, sig2n_k=True,\
spat_diag=False, vm=False, constant_regi='many',\
cols2regi='all', regime_err_sep=True, name_y=None, name_x=None,\
cores=None, name_yend=None, name_q=None, name_regimes=None,\
name_w=None, name_gwk=None, name_ds=None, summ=True):
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.constant_regi = constant_regi
self.cols2regi = cols2regi
self.name_ds = USER.set_name_ds(name_ds)
self.name_regimes = USER.set_name_ds(name_regimes)
self.name_w = USER.set_name_w(name_w, w)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
self.name_y = USER.set_name_y(name_y)
name_yend = USER.set_name_yend(name_yend, yend)
name_q = USER.set_name_q(name_q, q)
self.name_x_r = USER.set_name_x(name_x, x) + name_yend
self.n = n
cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x, yend=yend, add_cons=False)
self.regimes_set = REGI._get_regimes_set(regimes)
self.regimes = regimes
USER.check_regimes(self.regimes_set)
self.regime_err_sep = regime_err_sep
if regime_err_sep == True and set(cols2regi) == set([True]) and constant_regi == 'many':
name_x = USER.set_name_x(name_x, x)
self.y = y
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
self._tsls_regimes_multi(x, yend, q, w_i, regi_ids, cores,\
gwk, sig2n_k, robust, spat_diag, vm, name_x, name_yend, name_q)
else:
name_x = USER.set_name_x(name_x, x,constant=True)
q, self.name_q = REGI.Regimes_Frame.__init__(self, q, \
regimes, constant_regi=None, cols2regi='all', names=name_q)
x, self.name_x = REGI.Regimes_Frame.__init__(self, x, \
regimes, constant_regi, cols2regi=cols2regi, names=name_x)
yend, self.name_yend = REGI.Regimes_Frame.__init__(self, yend, \
regimes, constant_regi=None, \
cols2regi=cols2regi, yend=True, names=name_yend)
BaseTSLS.__init__(self, y=y, x=x, yend=yend, q=q, \
robust=robust, gwk=gwk, sig2n_k=sig2n_k)
if regime_err_sep == True and robust == None:
"""
# Weighted x, y, yend and q approach:
y2,x2,yend2,q2 = REGI._get_weighted_var(regimes,self.regimes_set,sig2n_k,self.u,y,x,yend,q)
tsls2 = BaseTSLS(y=y2, x=x2, yend=yend2, q=q2, sig2n_k=sig2n_k)
# Updating S_hat to S_tilde approach:
betas2, predy2, resid2, vm2 = self._optimal_weight(sig2n_k)
RegressionProps_basic(self,betas=betas2,predy=predy2,u=resid2,vm=vm2,sig2=False)
"""
betas2, vm2 = self._optimal_weight(sig2n_k)
RegressionProps_basic(self,betas=betas2,vm=vm2,sig2=False)
self.title = "TWO STAGE LEAST SQUARES - REGIMES (Optimal-Weighted GMM)"
robust = None
set_warn(self,"Residuals treated as homoskedastic for the purpose of diagnostics.")
else:
self.title = "TWO STAGE LEAST SQUARES - REGIMES"
self.name_z = self.name_x + self.name_yend
self.name_h = USER.set_name_h(self.name_x, self.name_q)
self.chow = REGI.Chow(self)
self.robust = USER.set_robust(robust)
if summ:
SUMMARY.TSLS(reg=self, vm=vm, w=w, spat_diag=spat_diag, regimes=True)
