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 GM_Lag_Regimes_Multi(self, y, x, w_i, regi_ids, cores=None,\
yend=None, q=None, w_lags=1, lag_q=True,\
robust=None, gwk=None, sig2n_k=False,cols2regi='all',\
spat_diag=False, vm=False, name_y=None, name_x=None,\
name_yend=None, name_q=None, name_regimes=None,\
name_w=None, name_gwk=None, name_ds=None):
pool = mp.Pool(cores)
self.name_ds = USER.set_name_ds(name_ds)
name_x = USER.set_name_x(name_x, x)
name_yend.append(USER.set_name_yend_sp(name_y))
self.name_w = USER.set_name_w(name_w, w_i)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
results_p = {}
for r in self.regimes_set:
w_r = w_i[r].sparse
results_p[r] = pool.apply_async(_work,args=(y,x,regi_ids,r,yend,q,w_r,w_lags,lag_q,robust,sig2n_k,self.name_ds,name_y,name_x,name_yend,name_q,self.name_w,name_regimes, ))
self.kryd = 0
self.kr = len(cols2regi) + 1
self.kf = 0
self.nr = len(self.regimes_set)
self.name_x_r = name_x + name_yend
self.name_regimes = name_regimes
self.vm = np.zeros((self.nr*self.kr,self.nr*self.kr),float)
self.betas = np.zeros((self.nr*self.kr,1),float)
self.u = np.zeros((self.n,1),float)
self.predy = np.zeros((self.n,1),float)
self.predy_e = np.zeros((self.n,1),float)
self.e_pred = np.zeros((self.n,1),float)
pool.close()
pool.join()
results = {}
self.name_y, self.name_x, self.name_yend, self.name_q, self.name_z, self.name_h = [],[],[],[],[],[]
counter = 0
for r in self.regimes_set:
results[r] = results_p[r].get()
results[r].predy_e, results[r].e_pred = sp_att(w_i[r],results[r].y,results[r].predy, results[r].yend[:,-1].reshape(results[r].n,1),results[r].betas[-1])
results[r].w = w_i[r]
self.vm[(counter*self.kr):((counter+1)*self.kr),(counter*self.kr):((counter+1)*self.kr)] = results[r].vm
self.betas[(counter*self.kr):((counter+1)*self.kr),] = results[r].betas
self.u[regi_ids[r],]=results[r].u
self.predy[regi_ids[r],]=results[r].predy
self.predy_e[regi_ids[r],]=results[r].predy_e
self.e_pred[regi_ids[r],]=results[r].e_pred
self.name_y += results[r].name_y
self.name_x += results[r].name_x
self.name_yend += results[r].name_yend
self.name_q += results[r].name_q
self.name_z += results[r].name_z
self.name_h += results[r].name_h
if r == self.regimes_set[0]:
self.hac_var = np.zeros((self.n,results[r].h.shape[1]),float)
self.hac_var[regi_ids[r],] = results[r].h
counter += 1
self.multi = results
if robust == 'hac':
hac_multi(self,gwk,constant=True)
self.chow = REGI.Chow(self)
SUMMARY.GM_Lag_multi(reg=self, multireg=self.multi, vm=vm, spat_diag=spat_diag, regimes=True)
def __init__(self, y, x, regimes, w=None, constant_regi='many',
cols2regi='all', method='full', epsilon=0.0000001,
regime_err_sep=False, regime_lag_sep=False, cores=False, spat_diag=False,
vm=False, name_y=None, name_x=None,
name_w=None, name_ds=None, name_regimes=None):
n = USER.check_arrays(y, x)
USER.check_y(y, n)
USER.check_weights(w, y, w_required=True)
self.constant_regi = constant_regi
self.cols2regi = cols2regi
self.regime_err_sep = regime_err_sep
self.name_ds = USER.set_name_ds(name_ds)
self.name_y = USER.set_name_y(name_y)
self.name_w = USER.set_name_w(name_w, w)
self.name_regimes = USER.set_name_ds(name_regimes)
self.n = n
self.y = y
x_constant = USER.check_constant(x)
name_x = USER.set_name_x(name_x, x)
self.name_x_r = name_x
cols2regi = REGI.check_cols2regi(constant_regi, cols2regi, x)
self.regimes_set = REGI._get_regimes_set(regimes)
self.regimes = regimes
USER.check_regimes(self.regimes_set, self.n, x.shape[1])
self.regime_err_sep = regime_err_sep
if regime_err_sep == True:
if set(cols2regi) == set([True]):
self._error_regimes_multi(y, x, regimes, w, cores,
method, epsilon, cols2regi, vm, name_x, spat_diag)
else:
raise Exception, "All coefficients must vary accross regimes if regime_err_sep = True."
else:
regimes_att = {}
regimes_att['x'] = x_constant
regimes_att['regimes'] = regimes
regimes_att['cols2regi'] = cols2regi
x, name_x = REGI.Regimes_Frame.__init__(self, x_constant,
regimes, constant_regi=None, cols2regi=cols2regi,
names=name_x)
BaseML_Error.__init__(
self, y=y, x=x, w=w, method=method, epsilon=epsilon, regimes_att=regimes_att)
self.title = "MAXIMUM LIKELIHOOD SPATIAL ERROR - REGIMES" + \
" (METHOD = " + method + ")"
self.name_x = USER.set_name_x(name_x, x, constant=True)
self.name_x.append('lambda')
self.kf += 1 # Adding a fixed k to account for lambda.
self.chow = REGI.Chow(self)
self.aic = DIAG.akaike(reg=self)
self.schwarz = DIAG.schwarz(reg=self)
self._cache = {}
SUMMARY.ML_Error(
reg=self, w=w, vm=vm, spat_diag=spat_diag, regimes=True)
def _tsls_regimes_multi(self, x, yend, q, w_i, regi_ids, cores,\
gwk, sig2n_k, robust, spat_diag, vm, name_x, name_yend, name_q):
pool = mp.Pool(cores)
results_p = {}
for r in self.regimes_set:
if system() == 'Windows':
is_win = True
results_p[r] = _work(*(self.y,x,regi_ids,r,yend,q,robust,sig2n_k,self.name_ds,self.name_y,name_x,name_yend,name_q,self.name_w,self.name_regimes))
else:
results_p[r] = pool.apply_async(_work,args=(self.y,x,regi_ids,r,yend,q,robust,sig2n_k,self.name_ds,self.name_y,name_x,name_yend,name_q,self.name_w,self.name_regimes))
is_win = False
self.kryd = 0
self.kr = x.shape[1]+yend.shape[1]+1
self.kf = 0
self.nr = len(self.regimes_set)
self.vm = np.zeros((self.nr*self.kr,self.nr*self.kr),float)
self.betas = np.zeros((self.nr*self.kr,1),float)
self.u = np.zeros((self.n,1),float)
self.predy = np.zeros((self.n,1),float)
if not is_win:
pool.close()
pool.join()
results = {}
self.name_y, self.name_x, self.name_yend, self.name_q, self.name_z, self.name_h = [],[],[],[],[],[]
counter = 0
for r in self.regimes_set:
if is_win:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
if w_i:
results[r].w = w_i[r]
else:
results[r].w = None
self.vm[(counter*self.kr):((counter+1)*self.kr),(counter*self.kr):((counter+1)*self.kr)] = results[r].vm
self.betas[(counter*self.kr):((counter+1)*self.kr),] = results[r].betas
self.u[regi_ids[r],]=results[r].u
self.predy[regi_ids[r],]=results[r].predy
self.name_y += results[r].name_y
self.name_x += results[r].name_x
self.name_yend += results[r].name_yend
self.name_q += results[r].name_q
self.name_z += results[r].name_z
self.name_h += results[r].name_h
counter += 1
self.multi = results
self.hac_var = sphstack(x,q)
if robust == 'hac':
hac_multi(self,gwk)
self.chow = REGI.Chow(self)
SUMMARY.TSLS_multi(reg=self, multireg=self.multi, vm=vm, spat_diag=spat_diag, 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 _ols_regimes_multi(self, x, w_i, regi_ids, cores,\
gwk, sig2n_k, robust, nonspat_diag, spat_diag, vm, name_x, moran):
pool = mp.Pool(cores)
results_p = {}
for r in self.regimes_set:
results_p[r] = pool.apply_async(_work,args=(self.y,x,regi_ids,r,robust,sig2n_k,self.name_ds,self.name_y,name_x,self.name_w,self.name_regimes))
self.kryd = 0
self.kr = x.shape[1]+1
self.kf = 0
self.nr = len(self.regimes_set)
self.vm = np.zeros((self.nr*self.kr,self.nr*self.kr),float)
self.betas = np.zeros((self.nr*self.kr,1),float)
self.u = np.zeros((self.n,1),float)
self.predy = np.zeros((self.n,1),float)
pool.close()
pool.join()
results = {}
self.name_y, self.name_x = [],[]
counter = 0
for r in self.regimes_set:
results[r] = results_p[r].get()
if w_i:
results[r].w = w_i[r]
else:
results[r].w = None
self.vm[(counter*self.kr):((counter+1)*self.kr),(counter*self.kr):((counter+1)*self.kr)] = results[r].vm
self.betas[(counter*self.kr):((counter+1)*self.kr),] = results[r].betas
self.u[regi_ids[r],]=results[r].u
self.predy[regi_ids[r],]=results[r].predy
self.name_y += results[r].name_y
self.name_x += results[r].name_x
counter += 1
self.multi = results
self.hac_var = x
if robust == 'hac':
hac_multi(self,gwk)
self.chow = REGI.Chow(self)
SUMMARY.OLS_multi(reg=self, multireg=self.multi, vm=vm, nonspat_diag=nonspat_diag, spat_diag=spat_diag, moran=moran, regimes=True)
def GM_Lag_Regimes_Multi(self,
y,
x,
w_i,
w,
regi_ids,
cores=False,
yend=None,
q=None,
w_lags=1,
lag_q=True,
robust=None,
gwk=None,
sig2n_k=False,
cols2regi='all',
spat_diag=False,
vm=False,
name_y=None,
name_x=None,
name_yend=None,
name_q=None,
name_regimes=None,
name_w=None,
name_gwk=None,
name_ds=None):
# pool = mp.Pool(cores)
self.name_ds = USER.set_name_ds(name_ds)
name_x = USER.set_name_x(name_x, x)
name_yend.append(USER.set_name_yend_sp(name_y))
self.name_w = USER.set_name_w(name_w, w_i)
self.name_gwk = USER.set_name_w(name_gwk, gwk)
results_p = {}
"""
for r in self.regimes_set:
w_r = w_i[r].sparse
if system() == 'Windows':
is_win = True
results_p[r] = _work(*(y,x,regi_ids,r,yend,q,w_r,w_lags,lag_q,robust,sig2n_k,self.name_ds,name_y,name_x,name_yend,name_q,self.name_w,name_regimes))
else:
results_p[r] = pool.apply_async(_work,args=(y,x,regi_ids,r,yend,q,w_r,w_lags,lag_q,robust,sig2n_k,self.name_ds,name_y,name_x,name_yend,name_q,self.name_w,name_regimes, ))
is_win = False
"""
for r in self.regimes_set:
w_r = w_i[r].sparse
if cores:
pool = mp.Pool(None)
results_p[r] = pool.apply_async(_work,
args=(
y,
x,
regi_ids,
r,
yend,
q,
w_r,
w_lags,
lag_q,
robust,
sig2n_k,
self.name_ds,
name_y,
name_x,
name_yend,
name_q,
self.name_w,
name_regimes,
))
else:
results_p[r] = _work(*(y, x, regi_ids, r, yend, q, w_r, w_lags,
lag_q, robust, sig2n_k, self.name_ds,
name_y, name_x, name_yend, name_q,
self.name_w, name_regimes))
self.kryd = 0
self.kr = len(cols2regi) + 1
self.kf = 0
self.nr = len(self.regimes_set)
self.name_x_r = name_x + name_yend
self.name_regimes = name_regimes
self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
self.betas = np.zeros((self.nr * self.kr, 1), float)
self.u = np.zeros((self.n, 1), float)
self.predy = np.zeros((self.n, 1), float)
self.predy_e = np.zeros((self.n, 1), float)
self.e_pred = np.zeros((self.n, 1), float)
"""
if not is_win:
pool.close()
pool.join()
"""
if cores:
pool.close()
pool.join()
results = {}
self.name_y, self.name_x, self.name_yend, self.name_q, self.name_z, self.name_h = [
], [], [], [], [], []
counter = 0
for r in self.regimes_set:
"""
if is_win:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
"""
if not cores:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
results[r].predy_e, results[r].e_pred, warn = sp_att(
w_i[r], results[r].y, results[r].predy,
results[r].yend[:, -1].reshape(results[r].n,
1), results[r].rho)
set_warn(results[r], warn)
results[r].w = w_i[r]
self.vm[(counter * self.kr):((counter + 1) * self.kr),
(counter * self.kr):((counter + 1) *
self.kr)] = results[r].vm
self.betas[(counter * self.kr):((counter + 1) *
self.kr), ] = results[r].betas
self.u[regi_ids[r], ] = results[r].u
self.predy[regi_ids[r], ] = results[r].predy
self.predy_e[regi_ids[r], ] = results[r].predy_e
self.e_pred[regi_ids[r], ] = results[r].e_pred
self.name_y += results[r].name_y
self.name_x += results[r].name_x
self.name_yend += results[r].name_yend
self.name_q += results[r].name_q
self.name_z += results[r].name_z
self.name_h += results[r].name_h
if r == self.regimes_set[0]:
self.hac_var = np.zeros((self.n, results[r].h.shape[1]), float)
self.hac_var[regi_ids[r], ] = results[r].h
counter += 1
self.multi = results
if robust == 'hac':
hac_multi(self, gwk, constant=True)
if robust == 'ogmm':
set_warn(
self,
"Residuals treated as homoskedastic for the purpose of diagnostics."
)
self.chow = REGI.Chow(self)
if spat_diag:
pass
#self._get_spat_diag_props(y, x, w, yend, q, w_lags, lag_q)
SUMMARY.GM_Lag_multi(reg=self,
multireg=self.multi,
vm=vm,
spat_diag=spat_diag,
regimes=True,
w=w)
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)
def ML_Lag_Regimes_Multi(self, y, x, w_i, w, regi_ids,
cores, cols2regi, method, epsilon,
spat_diag, vm, name_y, name_x,
name_regimes, name_w, name_ds):
# pool = mp.Pool(cores)
name_x = USER.set_name_x(name_x, x) + [USER.set_name_yend_sp(name_y)]
results_p = {}
"""
for r in self.regimes_set:
if system() == 'Windows':
is_win = True
results_p[r] = _work(*(y,x,regi_ids,r,w_i[r],method,epsilon,name_ds,name_y,name_x,name_w,name_regimes))
else:
results_p[r] = pool.apply_async(_work,args=(y,x,regi_ids,r,w_i[r],method,epsilon,name_ds,name_y,name_x,name_w,name_regimes, ))
is_win = False
"""
for r in self.regimes_set:
if cores:
pool = mp.Pool(None)
results_p[r] = pool.apply_async(_work, args=(y, x, regi_ids, r, w_i[
r], method, epsilon, name_ds, name_y, name_x, name_w, name_regimes, ))
else:
results_p[r] = _work(
*(y, x, regi_ids, r, w_i[r], method, epsilon, name_ds, name_y, name_x, name_w, name_regimes))
self.kryd = 0
self.kr = len(cols2regi) + 1
self.kf = 0
self.nr = len(self.regimes_set)
self.name_x_r = name_x
self.name_regimes = name_regimes
self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
self.betas = np.zeros((self.nr * self.kr, 1), float)
self.u = np.zeros((self.n, 1), float)
self.predy = np.zeros((self.n, 1), float)
self.predy_e = np.zeros((self.n, 1), float)
self.e_pred = np.zeros((self.n, 1), float)
"""
if not is_win:
pool.close()
pool.join()
"""
if cores:
pool.close()
pool.join()
results = {}
self.name_y, self.name_x = [], []
counter = 0
for r in self.regimes_set:
"""
if is_win:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
"""
if not cores:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
self.vm[(counter * self.kr):((counter + 1) * self.kr),
(counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
self.betas[
(counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
self.u[regi_ids[r], ] = results[r].u
self.predy[regi_ids[r], ] = results[r].predy
self.predy_e[regi_ids[r], ] = results[r].predy_e
self.e_pred[regi_ids[r], ] = results[r].e_pred
self.name_y += results[r].name_y
self.name_x += results[r].name_x
counter += 1
self.multi = results
self.chow = REGI.Chow(self)
SUMMARY.ML_Lag_multi(
reg=self, multireg=self.multi, vm=vm, spat_diag=spat_diag, regimes=True, w=w)
def __init__(self, y, x, regimes, w=None, constant_regi='many',
cols2regi='all', method='full', epsilon=0.0000001,
regime_lag_sep=False, regime_err_sep=False, cores=False, spat_diag=False,
vm=False, name_y=None, name_x=None,
name_w=None, name_ds=None, name_regimes=None):
n = USER.check_arrays(y, x)
USER.check_y(y, n)
USER.check_weights(w, y, w_required=True)
USER.check_spat_diag(spat_diag, w)
name_y = USER.set_name_y(name_y)
self.name_y = name_y
self.name_x_r = USER.set_name_x(
name_x, x) + [USER.set_name_yend_sp(name_y)]
self.method = method
self.epsilon = epsilon
self.name_regimes = USER.set_name_ds(name_regimes)
self.constant_regi = constant_regi
self.n = n
cols2regi = REGI.check_cols2regi(
constant_regi, cols2regi, x, add_cons=False)
self.cols2regi = cols2regi
self.regimes_set = REGI._get_regimes_set(regimes)
self.regimes = regimes
self.regime_lag_sep = regime_lag_sep
self._cache = {}
self.name_ds = USER.set_name_ds(name_ds)
self.name_w = USER.set_name_w(name_w, w)
USER.check_regimes(self.regimes_set, self.n, x.shape[1])
# regime_err_sep is ignored, always False
if regime_lag_sep == True:
if not (set(cols2regi) == set([True]) and constant_regi == 'many'):
raise Exception, "All variables must vary by regimes if regime_lag_sep = True."
cols2regi += [True]
w_i, regi_ids, warn = REGI.w_regimes(
w, regimes, self.regimes_set, transform=True, get_ids=True, min_n=len(cols2regi) + 1)
set_warn(self, warn)
else:
cols2regi += [False]
if set(cols2regi) == set([True]) and constant_regi == 'many':
self.y = y
self.ML_Lag_Regimes_Multi(y, x, w_i, w, regi_ids,
cores=cores, cols2regi=cols2regi, method=method, epsilon=epsilon,
spat_diag=spat_diag, vm=vm, name_y=name_y, name_x=name_x,
name_regimes=self.name_regimes,
name_w=name_w, name_ds=name_ds)
else:
# if regime_lag_sep == True:
# w = REGI.w_regimes_union(w, w_i, self.regimes_set)
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=cols2regi[:-1], names=name_x)
self.name_x.append("_Global_" + USER.set_name_yend_sp(name_y))
BaseML_Lag.__init__(
self, y=y, x=x, w=w, method=method, epsilon=epsilon)
self.kf += 1 # Adding a fixed k to account for spatial lag in Chow
# adding a fixed k to account for spatial lag in aic, sc
self.k += 1
self.chow = REGI.Chow(self)
self.aic = DIAG.akaike(reg=self)
self.schwarz = DIAG.schwarz(reg=self)
self.regime_lag_sep = regime_lag_sep
self.title = "MAXIMUM LIKELIHOOD SPATIAL LAG - REGIMES" + \
" (METHOD = " + method + ")"
SUMMARY.ML_Lag(
reg=self, w=w, vm=vm, spat_diag=spat_diag, regimes=True)
def _error_regimes_multi(self, y, x, regimes, w, cores,
method, epsilon, cols2regi, vm, name_x, spat_diag):
regi_ids = dict(
(r, list(np.where(np.array(regimes) == r)[0])) for r in self.regimes_set)
results_p = {}
"""
for r in self.regimes_set:
if system() == 'Windows':
is_win = True
results_p[r] = _work_error(*(y,x,regi_ids,r,w,method,epsilon,self.name_ds,self.name_y,name_x+['lambda'],self.name_w,self.name_regimes))
else:
pool = mp.Pool(cores)
results_p[r] = pool.apply_async(_work_error,args=(y,x,regi_ids,r,w,method,epsilon,self.name_ds,self.name_y,name_x+['lambda'],self.name_w,self.name_regimes, ))
is_win = False
"""
for r in self.regimes_set:
if cores:
pool = mp.Pool(None)
results_p[r] = pool.apply_async(_work_error, args=(
y, x, regi_ids, r, w, method, epsilon, self.name_ds, self.name_y, name_x + ['lambda'], self.name_w, self.name_regimes, ))
else:
results_p[r] = _work_error(
*(y, x, regi_ids, r, w, method, epsilon, self.name_ds, self.name_y, name_x + ['lambda'], self.name_w, self.name_regimes))
self.kryd = 0
self.kr = len(cols2regi) + 1
self.kf = 0
self.nr = len(self.regimes_set)
self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
self.betas = np.zeros((self.nr * self.kr, 1), float)
self.u = np.zeros((self.n, 1), float)
self.predy = np.zeros((self.n, 1), float)
self.e_filtered = np.zeros((self.n, 1), float)
self.name_y, self.name_x = [], []
"""
if not is_win:
pool.close()
pool.join()
"""
if cores:
pool.close()
pool.join()
results = {}
counter = 0
for r in self.regimes_set:
"""
if is_win:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
"""
if not cores:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
self.vm[(counter * self.kr):((counter + 1) * self.kr),
(counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
self.betas[
(counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
self.u[regi_ids[r], ] = results[r].u
self.predy[regi_ids[r], ] = results[r].predy
self.e_filtered[regi_ids[r], ] = results[r].e_filtered
self.name_y += results[r].name_y
self.name_x += results[r].name_x
counter += 1
self.chow = REGI.Chow(self)
self.multi = results
SUMMARY.ML_Error_multi(
reg=self, multireg=self.multi, vm=vm, spat_diag=spat_diag, regimes=True, w=w)
def _ols_regimes_multi(self, x, w, regi_ids, cores,
gwk, sig2n_k, robust, nonspat_diag, spat_diag, vm, name_x, moran, white_test):
results_p = {}
"""
for r in self.regimes_set:
if system() == 'Windows':
is_win = True
results_p[r] = _work(*(self.y,x,w,regi_ids,r,robust,sig2n_k,self.name_ds,self.name_y,name_x,self.name_w,self.name_regimes))
else:
pool = mp.Pool(cores)
results_p[r] = pool.apply_async(_work,args=(self.y,x,w,regi_ids,r,robust,sig2n_k,self.name_ds,self.name_y,name_x,self.name_w,self.name_regimes))
is_win = False
"""
for r in self.regimes_set:
if cores:
pool = mp.Pool(None)
results_p[r] = pool.apply_async(_work, args=(
self.y, x, w, regi_ids, r, robust, sig2n_k, self.name_ds, self.name_y, name_x, self.name_w, self.name_regimes))
else:
results_p[r] = _work(*(self.y, x, w, regi_ids, r, robust, sig2n_k,
self.name_ds, self.name_y, name_x, self.name_w, self.name_regimes))
self.kryd = 0
self.kr = x.shape[1] + 1
self.kf = 0
self.nr = len(self.regimes_set)
self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
self.betas = np.zeros((self.nr * self.kr, 1), float)
self.u = np.zeros((self.n, 1), float)
self.predy = np.zeros((self.n, 1), float)
"""
if not is_win:
pool.close()
pool.join()
"""
if cores:
pool.close()
pool.join()
results = {}
self.name_y, self.name_x = [], []
counter = 0
for r in self.regimes_set:
"""
if is_win:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
"""
if not cores:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
self.vm[(counter * self.kr):((counter + 1) * self.kr),
(counter * self.kr):((counter + 1) * self.kr)] = results[r].vm
self.betas[
(counter * self.kr):((counter + 1) * self.kr), ] = results[r].betas
self.u[regi_ids[r], ] = results[r].u
self.predy[regi_ids[r], ] = results[r].predy
self.name_y += results[r].name_y
self.name_x += results[r].name_x
counter += 1
self.multi = results
self.hac_var = x
if robust == 'hac':
hac_multi(self, gwk)
self.chow = REGI.Chow(self)
if spat_diag:
self._get_spat_diag_props(x, sig2n_k)
SUMMARY.OLS_multi(reg=self, multireg=self.multi, vm=vm, nonspat_diag=nonspat_diag,
spat_diag=spat_diag, moran=moran, white_test=white_test, regimes=True, w=w)
def _tsls_regimes_multi(self, x, yend, q, w, regi_ids, cores, gwk, sig2n_k,
robust, spat_diag, vm, name_x, name_yend, name_q):
results_p = {}
"""
for r in self.regimes_set:
if system() != 'Windows':
is_win = True
results_p[r] = _work(*(self.y,x,w,regi_ids,r,yend,q,robust,sig2n_k,self.name_ds,self.name_y,name_x,name_yend,name_q,self.name_w,self.name_regimes))
else:
pool = mp.Pool(cores)
results_p[r] = pool.apply_async(_work,args=(self.y,x,w,regi_ids,r,yend,q,robust,sig2n_k,self.name_ds,self.name_y,name_x,name_yend,name_q,self.name_w,self.name_regimes))
is_win = False
"""
for r in self.regimes_set:
if cores:
pool = mp.Pool(None)
results_p[r] = pool.apply_async(
_work,
args=(self.y, x, w, regi_ids, r, yend, q, robust, sig2n_k,
self.name_ds, self.name_y, name_x, name_yend, name_q,
self.name_w, self.name_regimes))
else:
results_p[r] = _work(*(self.y, x, w, regi_ids, r, yend, q,
robust, sig2n_k, self.name_ds,
self.name_y, name_x, name_yend, name_q,
self.name_w, self.name_regimes))
self.kryd = 0
self.kr = x.shape[1] + yend.shape[1] + 1
self.kf = 0
self.nr = len(self.regimes_set)
self.vm = np.zeros((self.nr * self.kr, self.nr * self.kr), float)
self.betas = np.zeros((self.nr * self.kr, 1), float)
self.u = np.zeros((self.n, 1), float)
self.predy = np.zeros((self.n, 1), float)
"""
if not is_win:
pool.close()
pool.join()
"""
if cores:
pool.close()
pool.join()
results = {}
self.name_y, self.name_x, self.name_yend, self.name_q, self.name_z, self.name_h = [
], [], [], [], [], []
counter = 0
for r in self.regimes_set:
"""
if is_win:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
"""
if not cores:
results[r] = results_p[r]
else:
results[r] = results_p[r].get()
self.vm[(counter * self.kr):((counter + 1) * self.kr),
(counter * self.kr):((counter + 1) *
self.kr)] = results[r].vm
self.betas[(counter * self.kr):((counter + 1) *
self.kr), ] = results[r].betas
self.u[regi_ids[r], ] = results[r].u
self.predy[regi_ids[r], ] = results[r].predy
self.name_y += results[r].name_y
self.name_x += results[r].name_x
self.name_yend += results[r].name_yend
self.name_q += results[r].name_q
self.name_z += results[r].name_z
self.name_h += results[r].name_h
counter += 1
self.multi = results
self.hac_var = sphstack(x, q)
if robust == 'hac':
hac_multi(self, gwk)
if robust == 'ogmm':
set_warn(
self,
"Residuals treated as homoskedastic for the purpose of diagnostics."
)
self.chow = REGI.Chow(self)
if spat_diag:
self._get_spat_diag_props(results, regi_ids, x, yend, q)
SUMMARY.TSLS_multi(reg=self,
multireg=self.multi,
vm=vm,
spat_diag=spat_diag,
regimes=True,
w=w)
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)
