def __init__(self,bigy,bigX,bigyend,bigq,nonspat_diag=True,\
name_bigy=None,name_bigX=None,name_bigyend=None,name_bigq=None,\
name_ds=None):
#need checks on match between bigy, bigX dimensions
BaseThreeSLS.__init__(self,bigy=bigy,bigX=bigX,bigyend=bigyend,\
bigq=bigq)
self.name_ds = USER.set_name_ds(name_ds)
#initialize names - should be generated by sur_stack
if name_bigy:
self.name_bigy = name_bigy
else: # need to construct y names
self.name_bigy = {}
for r in range(self.n_eq):
yn = 'dep_var_' + str(r+1)
self.name_bigy[r] = yn
if name_bigX:
self.name_bigX = name_bigX
else: # need to construct x names
self.name_bigX = {}
for r in range(self.n_eq):
k = bigX[r].shape[1] - 1
name_x = ['var_' + str(i + 1) + "_" + str(r+1) for i in range(k)]
ct = 'Constant_' + str(r+1) # NOTE: constant always included in X
name_x.insert(0, ct)
self.name_bigX[r] = name_x
if name_bigyend:
self.name_bigyend = name_bigyend
else: # need to construct names
self.name_bigyend = {}
for r in range(self.n_eq):
ky = bigyend[r].shape[1]
name_ye = ['end_' + str(i + 1) + "_" + str(r+1) for i in range(ky)]
self.name_bigyend[r] = name_ye
if name_bigq:
self.name_bigq = name_bigq
else: # need to construct names
self.name_bigq = {}
for r in range(self.n_eq):
ki = bigq[r].shape[1]
name_i = ['inst_' + str(i + 1) + "_" + str(r+1) for i in range(ki)]
self.name_bigq[r] = name_i
#inference
self.tsls_inf = sur_setp(self.b3SLS,self.varb)
# test on constancy of coefficients across equations
if check_k(self.bigK): # only for equal number of variables
self.surchow = sur_chow(self.n_eq,self.bigK,self.b3SLS,self.varb)
else:
self.surchow = None
#Listing of the results
self.title = "THREE STAGE LEAST SQUARES (3SLS)"
SUMMARY.SUR(reg=self, tsls=True, nonspat_diag=nonspat_diag)
def __init__(self,bigy,bigX,bigyend,bigq,nonspat_diag=True,\
name_bigy=None,name_bigX=None,name_bigyend=None,name_bigq=None,\
name_ds=None):
#need checks on match between bigy, bigX dimensions
BaseThreeSLS.__init__(self,bigy=bigy,bigX=bigX,bigyend=bigyend,\
bigq=bigq)
self.name_ds = USER.set_name_ds(name_ds)
#initialize names - should be generated by sur_stack
if name_bigy:
self.name_bigy = name_bigy
else: # need to construct y names
self.name_bigy = {}
for r in range(self.n_eq):
yn = 'dep_var_' + str(r+1)
self.name_bigy[r] = yn
if name_bigX:
self.name_bigX = name_bigX
else: # need to construct x names
self.name_bigX = {}
for r in range(self.n_eq):
k = bigX[r].shape[1] - 1
name_x = ['var_' + str(i + 1) + "_" + str(r+1) for i in range(k)]
ct = 'Constant_' + str(r+1) # NOTE: constant always included in X
name_x.insert(0, ct)
self.name_bigX[r] = name_x
if name_bigyend:
self.name_bigyend = name_bigyend
else: # need to construct names
self.name_bigyend = {}
for r in range(self.n_eq):
ky = bigyend[r].shape[1]
name_ye = ['end_' + str(i + 1) + "_" + str(r+1) for i in range(ky)]
self.name_bigyend[r] = name_ye
if name_bigq:
self.name_bigq = name_bigq
else: # need to construct names
self.name_bigq = {}
for r in range(self.n_eq):
ki = bigq[r].shape[1]
name_i = ['inst_' + str(i + 1) + "_" + str(r+1) for i in range(ki)]
self.name_bigq[r] = name_i
#inference
self.tsls_inf = sur_setp(self.b3SLS,self.varb)
# test on constancy of coefficients across equations
if check_k(self.bigK): # only for equal number of variables
self.surchow = sur_chow(self.n_eq,self.bigK,self.b3SLS,self.varb)
else:
self.surchow = None
#Listing of the results
self.title = "THREE STAGE LEAST SQUARES (3SLS)"
SUMMARY.SUR(reg=self, tsls=True, nonspat_diag=nonspat_diag)
def __init__(self,bigy,bigX,w=None,nonspat_diag=True,spat_diag=False,vm=False,\
iter=False,maxiter=5,epsilon=0.00001,verbose=False,\
name_bigy=None,name_bigX=None,name_ds=None,name_w=None):
#need checks on match between bigy, bigX dimensions
# init moved here before name check
BaseSUR.__init__(self,bigy=bigy,bigX=bigX,iter=iter,\
maxiter=maxiter,epsilon=epsilon,verbose=verbose)
self.name_ds = USER.set_name_ds(name_ds)
self.name_w = USER.set_name_w(name_w, w)
#initialize names - should be generated by sur_stack
if name_bigy:
self.name_bigy = name_bigy
else: # need to construct y names
self.name_bigy = {}
for r in range(self.n_eq):
yn = 'dep_var_' + str(r)
self.name_bigy[r] = yn
if name_bigX:
self.name_bigX = name_bigX
else: # need to construct x names
self.name_bigX = {}
for r in range(self.n_eq):
k = self.bigX[r].shape[1] - 1
name_x = ['var_' + str(i + 1) + "_" + str(r) for i in range(k)]
ct = 'Constant_' + str(
r) # NOTE: constant always included in X
name_x.insert(0, ct)
self.name_bigX[r] = name_x
#inference
self.sur_inf = sur_setp(self.bSUR, self.varb)
if nonspat_diag:
#LR test on off-diagonal elements of Sigma
self.lrtest = sur_lrtest(self.n, self.n_eq, self.ldetS0,
self.ldetS1)
#LM test on off-diagonal elements of Sigma
self.lmtest = sur_lmtest(self.n, self.n_eq, self.sig_ols)
else:
self.lrtest = None
self.lmtest = None
#LM test on spatial error autocorrelation
if spat_diag:
if not w:
raise Exception, "Error: spatial weights needed"
WS = w.sparse
self.lmEtest = surLMe(self.n_eq, WS, self.bigE, self.sig)
else:
self.lmEtest = None
#LM test on spatial lag autocorrelation
# test on constancy of coefficients across equations
if check_k(self.bigK): # only for equal number of variables
self.surchow = sur_chow(self.n_eq, self.bigK, self.bSUR, self.varb)
else:
self.surchow = None
#Listing of the results
self.title = "SEEMINGLY UNRELATED REGRESSIONS (SUR)"
SUMMARY.SUR(reg=self,
nonspat_diag=nonspat_diag,
spat_diag=spat_diag,
surlm=True)
def __init__(self,bigy,bigX,w=None,nonspat_diag=True,spat_diag=False,vm=False,\
iter=False,maxiter=5,epsilon=0.00001,verbose=False,\
name_bigy=None,name_bigX=None,name_ds=None,name_w=None):
#need checks on match between bigy, bigX dimensions
# init moved here before name check
BaseSUR.__init__(self,bigy=bigy,bigX=bigX,iter=iter,\
maxiter=maxiter,epsilon=epsilon,verbose=verbose)
self.name_ds = USER.set_name_ds(name_ds)
self.name_w = USER.set_name_w(name_w, w)
#initialize names - should be generated by sur_stack
if name_bigy:
self.name_bigy = name_bigy
else: # need to construct y names
self.name_bigy = {}
for r in range(self.n_eq):
yn = 'dep_var_' + str(r)
self.name_bigy[r] = yn
if name_bigX:
self.name_bigX = name_bigX
else: # need to construct x names
self.name_bigX = {}
for r in range(self.n_eq):
k = self.bigX[r].shape[1] - 1
name_x = ['var_' + str(i + 1) + "_" + str(r) for i in range(k)]
ct = 'Constant_' + str(r) # NOTE: constant always included in X
name_x.insert(0, ct)
self.name_bigX[r] = name_x
#inference
self.sur_inf = sur_setp(self.bSUR,self.varb)
if nonspat_diag:
#LR test on off-diagonal elements of Sigma
self.lrtest = sur_lrtest(self.n,self.n_eq,self.ldetS0,self.ldetS1)
#LM test on off-diagonal elements of Sigma
self.lmtest = sur_lmtest(self.n,self.n_eq,self.sig_ols)
else:
self.lrtest = None
self.lmtest = None
#LM test on spatial error autocorrelation
if spat_diag:
if not w:
raise Exception, "Error: spatial weights needed"
WS = w.sparse
self.lmEtest = surLMe(self.n_eq,WS,self.bigE,self.sig)
else:
self.lmEtest = None
#LM test on spatial lag autocorrelation
# test on constancy of coefficients across equations
if check_k(self.bigK): # only for equal number of variables
self.surchow = sur_chow(self.n_eq,self.bigK,self.bSUR,self.varb)
else:
self.surchow = None
#Listing of the results
self.title = "SEEMINGLY UNRELATED REGRESSIONS (SUR)"
SUMMARY.SUR(reg=self, nonspat_diag=nonspat_diag, spat_diag=spat_diag, surlm=True)
def __init__(self,bigy,bigX,w,nonspat_diag=True,spat_diag=False,vm=False,\
epsilon=0.0000001,\
name_bigy=None,name_bigX=None,name_ds=None,name_w=None):
#need checks on match between bigy, bigX dimensions
# moved init here
BaseSURerrorML.__init__(self,bigy=bigy,bigX=bigX,w=w,epsilon=epsilon)
# check on variable names for listing results
self.name_ds = USER.set_name_ds(name_ds)
self.name_w = USER.set_name_w(name_w, w)
#initialize names - should be generated by sur_stack
if name_bigy:
self.name_bigy = name_bigy
else: # need to construct y names
self.name_bigy = {}
for r in range(self.n_eq):
yn = 'dep_var_' + str(r)
self.name_bigy[r] = yn
if name_bigX:
self.name_bigX = name_bigX
else: # need to construct x names
self.name_bigX = {}
for r in range(self.n_eq):
k = self.bigX[r].shape[1] - 1
name_x = ['var_' + str(i + 1) + "_" + str(r+1) for i in range(k)]
ct = 'Constant_' + str(r+1) # NOTE: constant always included in X
name_x.insert(0, ct)
self.name_bigX[r] = name_x
#inference
self.sur_inf = sur_setp(self.bSUR,self.varb)
# adjust concentrated log lik for constant
const = -self.n2 * (self.n_eq * (1.0 + np.log(2.0*np.pi)))
self.errllik = const + self.clikerr
self.surerrllik = const + self.cliksurerr
# LR test on off-diagonal sigma
if nonspat_diag:
M = self.n_eq * (self.n_eq - 1)/2.0
likrodiag = 2.0 * (self.surerrllik - self.errllik)
plik1 = stats.chisqprob(likrodiag, M)
self.lrtest = (likrodiag,int(M),plik1)
else:
self.lrtest = None
# LR test on spatial autoregressive coefficients
if spat_diag:
liklambda = 2.0 * (self.surerrllik - self.llik)
plik2 = stats.chisqprob(liklambda, self.n_eq)
self.likrlambda = (liklambda,self.n_eq,plik2)
else:
self.likrlambda = None
# asymptotic variance for spatial coefficient
if vm:
self.vm = surerrvm(self.n,self.n_eq,w,self.lamsur,self.sig)
vlam = self.vm[:self.n_eq,:self.n_eq]
self.lamsetp = lam_setp(self.lamsur,vlam)
# test on constancy of lambdas
R = buildR(kr=1,kf=0,nr=self.n_eq)
w,p = wald_test(self.lamsur,R,np.zeros((R.shape[0],1)),vlam)
self.lamtest = (w,R.shape[0],p)
if spat_diag: # test on joint significance of lambdas
Rj = np.identity(self.n_eq)
wj,pj = wald_test(self.lamsur,Rj,np.zeros((Rj.shape[0],1)),vlam)
self.joinlam = (wj,Rj.shape[0],pj)
else:
self.joinlam = None
else:
self.vm = None
self.lamsetp = None
self.lamtest = None
self.joinlam = None
# test on constancy of regression coefficients across equations
if check_k(self.bigK): # only for equal number of variables
self.surchow = sur_chow(self.n_eq,self.bigK,self.bSUR,self.varb)
else:
self.surchow = None
# listing of results
self.title = "SEEMINGLY UNRELATED REGRESSIONS (SUR) - SPATIAL ERROR MODEL"
SUMMARY.SUR(reg=self, nonspat_diag=nonspat_diag, spat_diag=spat_diag, lambd=True)
def __init__(self,bigy,bigX,bigyend=None,bigq=None,w=None,vm=False,\
w_lags=1, lag_q=True, nonspat_diag=True, spat_diag=False,\
name_bigy=None,name_bigX=None,name_bigyend=None,\
name_bigq=None,name_ds=None,name_w=None):
if w == None:
raise Exception, "Spatial weights required for SUR-Lag"
self.w = w
WS = w.sparse
self.name_ds = USER.set_name_ds(name_ds)
self.name_w = USER.set_name_w(name_w, w)
if bigyend and not(bigq):
raise Exception, "Instruments needed when endogenous variables"
#initialize
self.bigy = bigy
self.n_eq = len(self.bigy.keys())
if name_bigy:
self.name_bigy = name_bigy
else: # need to construct y names
self.name_bigy = {}
for r in range(self.n_eq):
yn = 'dep_var_' + str(r+1)
self.name_bigy[r] = yn
self.bigX = bigX
if name_bigX:
self.name_bigX = name_bigX
else: # need to construct x names
self.name_bigX = {}
for r in range(self.n_eq):
k = self.bigX[r].shape[1] - 1
name_x = ['var_' + str(i + 1) + "_" + str(r+1) for i in range(k)]
ct = 'Constant_' + str(r+1) # NOTE: constant always included in X
name_x.insert(0, ct)
self.name_bigX[r] = name_x
if bigyend: # check on other endogenous
self.bigyend = bigyend
if name_bigyend:
self.name_bigyend = name_bigyend
else: # need to construct names
self.name_bigyend = {}
for r in range(self.n_eq):
ky = self.bigyend[r].shape[1]
name_ye = ['end_' + str(i + 1) + "_" + str(r+1) for i in range(ky)]
self.name_bigyend[r] = name_ye
if bigq: # check on instruments
self.bigq = bigq
if name_bigq:
self.name_bigq = name_bigq
else: # need to construct names
self.name_bigq = {}
for r in range(self.n_eq):
ki = self.bigq[r].shape[1]
name_i = ['inst_' + str(i + 1) + "_" + str(r+1) for i in range(ki)]
self.name_bigq[r] = name_i
#spatial lag dependent variable
bigylag = {}
for r in range(self.n_eq):
bigylag[r] = WS*self.bigy[r]
if bigyend:
self.bigyend=bigyend
for r in range(self.n_eq):
self.bigyend[r] = np.hstack((self.bigyend[r],bigylag[r]))
# adjust variable names
for r in range(self.n_eq):
wyname = "W_" + self.name_bigy[r]
self.name_bigyend[r].append(wyname)
else: # no other endogenous variables
self.bigyend={}
for r in range(self.n_eq):
self.bigyend[r] = bigylag[r]
# variable names
self.name_bigyend = {}
for r in range(self.n_eq):
wyname = ["W_" + self.name_bigy[r]]
self.name_bigyend[r] = wyname
#spatially lagged exogenous variables
bigwx = {}
wxnames = {}
if w_lags == 1:
for r in range(self.n_eq):
bigwx[r] = WS* self.bigX[r][:,1:]
wxnames[r] = [ "W_" + i for i in self.name_bigX[r][1:]]
if bigq: # other instruments
if lag_q: # also lags for instruments
bigwq = {}
for r in range(self.n_eq):
bigwq = WS* self.bigq[r]
self.bigq[r] = np.hstack((self.bigq[r],bigwx[r],bigwq))
wqnames = [ "W_" + i for i in self.name_bigq[r]]
wxnames[r] = wxnames[r] + wqnames
self.name_bigq[r] = self.name_bigq[r] + wxnames[r]
else: # no lags for other instruments
for r in range(self.n_eq):
self.bigq[r] = np.hstack((self.bigq[r],bigwx[r]))
self.name_bigq[r] = self.name_bigq[r] + wxnames[r]
else: #no other instruments only wx
self.bigq = {}
self.name_bigq = {}
for r in range(self.n_eq):
self.bigq[r]=bigwx[r]
self.name_bigq[r] = wxnames[r]
elif w_lags > 1: # higher order lags for WX
for r in range(self.n_eq):
bigwxwork = WS* self.bigX[r][:,1:]
bigwx[r] = bigwxwork
nameswork = [ "W_" + i for i in self.name_bigX[r][1:]]
wxnames[r] = nameswork
for i in range(1,w_lags):
bigwxwork = WS*bigwxwork
bigwx[r] = np.hstack((bigwx[r],bigwxwork))
nameswork = [ "W" + i for i in nameswork ]
wxnames[r] = wxnames[r] + nameswork
if bigq: # other instruments
if lag_q: # lags for other instruments
wq = {}
wqnames = {}
for r in range(self.n_eq):
bigwq = WS* self.bigq[r]
wqnameswork = [ "W_" + i for i in self.name_bigq[r]]
wqnames[r] = wqnameswork
wq[r] = bigwq
for i in range(1,w_lags):
bigwq = WS* bigwq
wq[r] = np.hstack((wq[r],bigwq))
wqnameswork = [ "W" + i for i in wqnameswork ]
wqnames[r] = wqnames[r] + wqnameswork
self.bigq[r] = np.hstack((self.bigq[r],bigwx[r],wq[r]))
self.name_bigq[r] = self.name_bigq[r] + wxnames[r] + wqnames[r]
else: # no lags for other instruments
for r in range(self.n_eq):
self.bigq[r] = np.hstack((self.bigq[r],bigwx[r]))
self.name_bigq[r] = self.name_bigq[r] + wxnames[r]
else: # no other instruments only wx
self.bigq = {}
self.name_bigq = {}
for r in range(self.n_eq):
self.bigq[r] = bigwx[r]
self.name_bigq[r] = wxnames[r]
else:
raise Exception, "Lag order must be 1 or higher"
BaseThreeSLS.__init__(self,bigy=self.bigy,bigX=self.bigX,bigyend=self.bigyend,\
bigq=self.bigq)
#inference
self.tsls_inf = sur_setp(self.b3SLS,self.varb)
# test on joint significance of spatial coefficients
if spat_diag:
self.joinrho = sur_joinrho(self.n_eq,self.bigK,self.b3SLS,self.varb)
else:
self.joinrho = None
# test on constancy of coefficients across equations
if check_k(self.bigK): # only for equal number of variables
self.surchow = sur_chow(self.n_eq,self.bigK,self.b3SLS,self.varb)
else:
self.surchow = None
#list results
self.title = "SEEMINGLY UNRELATED REGRESSIONS (SUR) - SPATIAL LAG MODEL"
SUMMARY.SUR(reg=self, tsls=True, spat_diag=spat_diag, nonspat_diag=nonspat_diag)
