def _stackX(self, k_ar, trend):
"""
Private method to build the RHS matrix for estimation.
Columns are trend terms then lags.
"""
endog = self.endog
X = lagmat(endog, maxlag=k_ar, trim='both')
k_trend = util.get_trendorder(trend)
if k_trend:
X = add_trend(X, prepend=True, trend=trend)
self.k_trend = k_trend
return X
def _make_arma_exog(endog, exog, trend):
k_trend = 1 # overwritten if no constant
if exog is None and trend == 'c': # constant only
exog = np.ones((len(endog), 1))
elif exog is not None and trend == 'c': # constant plus exogenous
exog = add_trend(exog, trend='c', prepend=True)
elif exog is not None and trend == 'nc':
# make sure it's not holding constant from last run
if exog.var() == 0:
exog = None
k_trend = 0
if trend == 'nc':
k_trend = 0
return k_trend, exog
def _make_arma_exog(endog, exog, trend):
k_trend = 1 # overwritten if no constant
if exog is None and trend == 'c': # constant only
exog = np.ones((len(endog),1))
elif exog is not None and trend == 'c': # constant plus exogenous
exog = add_trend(exog, trend='c', prepend=True)
elif exog is not None and trend == 'nc':
# make sure it's not holding constant from last run
if exog.var() == 0:
exog = None
k_trend = 0
if trend == 'nc':
k_trend = 0
return k_trend, exog
def get_var_endog(y, lags, trend='c'):
"""
Make predictor matrix for VAR(p) process
Z := (Z_0, ..., Z_T).T (T x Kp)
Z_t = [1 y_t y_{t-1} ... y_{t - p + 1}] (Kp x 1)
Ref: Lutkepohl p.70 (transposed)
"""
nobs = len(y)
# Ravel C order, need to put in descending order
Z = np.array([y[t - lags:t][::-1].ravel() for t in xrange(lags, nobs)])
# Add constant, trend, etc.
if trend != 'nc':
Z = tsa.add_trend(Z, prepend=True, trend=trend)
return Z
def get_var_endog(y, lags, trend="c"):
"""
Make predictor matrix for VAR(p) process
Z := (Z_0, ..., Z_T).T (T x Kp)
Z_t = [1 y_t y_{t-1} ... y_{t - p + 1}] (Kp x 1)
Ref: Lutkepohl p.70 (transposed)
"""
nobs = len(y)
# Ravel C order, need to put in descending order
Z = np.array([y[t - lags : t][::-1].ravel() for t in xrange(lags, nobs)])
# Add constant, trend, etc.
if trend != "nc":
Z = tsa.add_trend(Z, prepend=True, trend=trend)
return Z
