def __init__(self, y, x, yend, q=None, h=None,
robust=None, gwk=None, sig2n_k=False):
if issubclass(type(q), np.ndarray) and issubclass(type(h), np.ndarray):
raise Exception, "Please do not provide 'q' and 'h' together"
if q == None and h == None:
raise Exception, "Please provide either 'q' or 'h'"
self.y = y
self.n = y.shape[0]
self.x = x
self.kstar = yend.shape[1]
# including exogenous and endogenous variables
z = sphstack(self.x, yend)
if type(h).__name__ not in ['ndarray', 'csr_matrix']:
# including exogenous variables and instrument
h = sphstack(self.x, q)
self.z = z
self.h = h
self.q = q
self.yend = yend
# k = number of exogenous variables and endogenous variables
self.k = z.shape[1]
hth = spdot(h.T, h)
hthi = la.inv(hth)
zth = spdot(z.T, h)
hty = spdot(h.T, y)
factor_1 = np.dot(zth, hthi)
factor_2 = np.dot(factor_1, zth.T)
# this one needs to be in cache to be used in AK
varb = la.inv(factor_2)
factor_3 = np.dot(varb, factor_1)
betas = np.dot(factor_3, hty)
self.betas = betas
self.varb = varb
self.zthhthi = factor_1
# predicted values
self.predy = spdot(z, betas)
# residuals
u = y - self.predy
self.u = u
# attributes used in property
self.hth = hth # Required for condition index
self.hthi = hthi # Used in error models
self.htz = zth.T
if robust:
self.vm = ROBUST.robust_vm(reg=self, gwk=gwk, sig2n_k=sig2n_k)
self._cache = {}
if sig2n_k:
self.sig2 = self.sig2n_k
else:
self.sig2 = self.sig2n
def __init__(self, y, x, yend, q=None, h=None,
robust=None, gwk=None, sig2n_k=False):
if issubclass(type(q), np.ndarray) and issubclass(type(h), np.ndarray):
raise Exception, "Please do not provide 'q' and 'h' together"
if q is None and h is None:
raise Exception, "Please provide either 'q' or 'h'"
self.y = y
self.n = y.shape[0]
self.x = x
self.kstar = yend.shape[1]
# including exogenous and endogenous variables
z = sphstack(self.x, yend)
if type(h).__name__ not in ['ndarray', 'csr_matrix']:
# including exogenous variables and instrument
h = sphstack(self.x, q)
self.z = z
self.h = h
self.q = q
self.yend = yend
# k = number of exogenous variables and endogenous variables
self.k = z.shape[1]
hth = spdot(h.T, h)
hthi = la.inv(hth)
zth = spdot(z.T, h)
hty = spdot(h.T, y)
factor_1 = np.dot(zth, hthi)
factor_2 = np.dot(factor_1, zth.T)
# this one needs to be in cache to be used in AK
varb = la.inv(factor_2)
factor_3 = np.dot(varb, factor_1)
betas = np.dot(factor_3, hty)
self.betas = betas
self.varb = varb
self.zthhthi = factor_1
# predicted values
self.predy = spdot(z, betas)
# residuals
u = y - self.predy
self.u = u
# attributes used in property
self.hth = hth # Required for condition index
self.hthi = hthi # Used in error models
self.htz = zth.T
if robust:
self.vm = ROBUST.robust_vm(reg=self, gwk=gwk, sig2n_k=sig2n_k)
self._cache = {}
if sig2n_k:
self.sig2 = self.sig2n_k
else:
self.sig2 = self.sig2n
def __init__(self, y, x, robust=None, gwk=None, sig2n_k=True):
self.x = x
self.xtx = spdot(self.x.T, self.x)
xty = spdot(self.x.T, y)
self.xtxi = la.inv(self.xtx)
self.betas = np.dot(self.xtxi, xty)
predy = spdot(self.x, self.betas)
u = y - predy
self.u = u
self.predy = predy
self.y = y
self.n, self.k = self.x.shape
if sig2n_k:
self.sig2 = self.sig2n_k
else:
self.sig2 = self.sig2n
if robust is not None:
self.vm = ROBUST.robust_vm(reg=self, gwk=gwk, sig2n_k=sig2n_k)
def __init__(self, y, x, robust=None, gwk=None, sig2n_k=True):
self.x = x
self.xtx = spdot(self.x.T, self.x)
xty = spdot(self.x.T, y)
self.xtxi = la.inv(self.xtx)
self.betas = np.dot(self.xtxi, xty)
predy = spdot(self.x, self.betas)
u = y - predy
self.u = u
self.predy = predy
self.y = y
self.n, self.k = self.x.shape
if sig2n_k:
self.sig2 = self.sig2n_k
else:
self.sig2 = self.sig2n
if robust is not None:
self.vm = ROBUST.robust_vm(reg=self, gwk=gwk, sig2n_k=sig2n_k)