def __init__(self, y, x, w):
#1a. OLS --> \tilde{betas}
ols = OLS.BaseOLS(y=y, x=x)
self.n, self.k = ols.x.shape
self.x = ols.x
self.y = ols.y
#1b. GMM --> \tilde{\lambda1}
moments = _momentsGM_Error(w, ols.u)
lambda1 = optim_moments(moments)
#2a. OLS -->\hat{betas}
xs = get_spFilter(w, lambda1, self.x)
ys = get_spFilter(w, lambda1, self.y)
ols2 = OLS.BaseOLS(y=ys, x=xs)
#Output
self.predy = spdot(self.x, ols2.betas)
self.u = y - self.predy
self.betas = np.vstack((ols2.betas, np.array([[lambda1]])))
self.sig2 = ols2.sig2n
self.e_filtered = self.u - lambda1*w*self.u
self.vm = self.sig2 * ols2.xtxi
se_betas = np.sqrt(self.vm.diagonal())
self._cache = {}
def __init__(self, y, x, yend, q, w):
#1a. TSLS --> \tilde{betas}
tsls = TSLS.BaseTSLS(y=y, x=x, yend=yend, q=q)
self.n, self.k = tsls.z.shape
self.x = tsls.x
self.y = tsls.y
self.yend, self.z = tsls.yend, tsls.z
#1b. GMM --> \tilde{\lambda1}
moments = _momentsGM_Error(w, tsls.u)
lambda1 = optim_moments(moments)
#2a. 2SLS -->\hat{betas}
xs = get_spFilter(w, lambda1, self.x)
ys = get_spFilter(w, lambda1, self.y)
yend_s = get_spFilter(w, lambda1, self.yend)
tsls2 = TSLS.BaseTSLS(ys, xs, yend_s, h=tsls.h)
#Output
self.betas = np.vstack((tsls2.betas, np.array([[lambda1]])))
self.predy = spdot(tsls.z, tsls2.betas)
self.u = y - self.predy
self.sig2 = float(np.dot(tsls2.u.T,tsls2.u)) / self.n
self.e_filtered = self.u - lambda1*w*self.u
self.vm = self.sig2 * tsls2.varb
self._cache = {}
def __init__(self, y, x, yend, q, w):
# 1a. TSLS --> \tilde{betas}
tsls = TSLS.BaseTSLS(y=y, x=x, yend=yend, q=q)
self.n, self.k = tsls.z.shape
self.x = tsls.x
self.y = tsls.y
self.yend, self.z = tsls.yend, tsls.z
# 1b. GMM --> \tilde{\lambda1}
moments = _momentsGM_Error(w, tsls.u)
lambda1 = optim_moments(moments)
# 2a. 2SLS -->\hat{betas}
xs = get_spFilter(w, lambda1, self.x)
ys = get_spFilter(w, lambda1, self.y)
yend_s = get_spFilter(w, lambda1, self.yend)
tsls2 = TSLS.BaseTSLS(ys, xs, yend_s, h=tsls.h)
# Output
self.betas = np.vstack((tsls2.betas, np.array([[lambda1]])))
self.predy = spdot(tsls.z, tsls2.betas)
self.u = y - self.predy
self.sig2 = float(np.dot(tsls2.u.T, tsls2.u)) / self.n
self.e_filtered = self.u - lambda1 * w * self.u
self.vm = self.sig2 * tsls2.varb
self._cache = {}
def __init__(self, y, x, w):
# 1a. OLS --> \tilde{betas}
ols = OLS.BaseOLS(y=y, x=x)
self.n, self.k = ols.x.shape
self.x = ols.x
self.y = ols.y
# 1b. GMM --> \tilde{\lambda1}
moments = _momentsGM_Error(w, ols.u)
lambda1 = optim_moments(moments)
# 2a. OLS -->\hat{betas}
xs = get_spFilter(w, lambda1, self.x)
ys = get_spFilter(w, lambda1, self.y)
ols2 = OLS.BaseOLS(y=ys, x=xs)
# Output
self.predy = spdot(self.x, ols2.betas)
self.u = y - self.predy
self.betas = np.vstack((ols2.betas, np.array([[lambda1]])))
self.sig2 = ols2.sig2n
self.e_filtered = self.u - lambda1 * w * self.u
self.vm = self.sig2 * ols2.xtxi
se_betas = np.sqrt(self.vm.diagonal())
self._cache = {}
#print('initial_lamb_sig:',lambda1,sig_v,sig_1)
#print('theta:', 1 - np.sqrt(sig_v)/ np.sqrt(sig_1))
Xi_a = SP.diags([(sig_v*sig_v)/(T-1),sig_1*sig_1])
if full_weights:
Tau = _get_Tau(w.sparse,trace_w2)
else:
Tau = SP.identity(3)
Xi = SP.kron(Xi_a,Tau)
moments_b,_ = _moments_kkp(w.sparse, ols.u, 1,trace_w2)
G = np.vstack((np.hstack((moments[0],np.zeros((3,1)))),moments_b[0]))
moments6 = [G,np.vstack((moments[1],moments_b[1]))]
lambda2,sig_vb,sig_1b = optim_moments(moments6, vcX=Xi.toarray(), all_par=True, start=[lambda1,sig_v,sig_1])
# 2a. reg -->\hat{betas}
theta = 1 - np.sqrt(sig_vb)/np.sqrt(sig_1b)
#print('theta:', theta)
gls_w = SP.identity(N*T) - theta*Q1
#With omega
xs = gls_w.dot(get_spFilter(w, lambda2, x))
ys = gls_w.dot(get_spFilter(w, lambda2, y))
ols_s = OLS.BaseOLS(y=ys, x=xs)
self.predy = spdot(self.x, ols_s.betas)
self.u = self.y - self.predy
self.vm = ols_s.vm #Check
self.betas = np.vstack((ols_s.betas, lambda2, sig_vb, sig_1b))
self.e_filtered = self.u - lambda2 * SP.kron(SP.identity(T),
w.sparse).dot(self.u)
self.t, self.n = T, N
self._cache = {}
