statsmodels.tsa.varma_process.VarmaPoly.stacksquare VarmaPoly.stacksquare(a=None, name='ar', orientation='vertical') [source] stack lagpolynomial vertically in 2d square array with eye

statsmodels.tsa.varma_process.VarmaPoly.reduceform VarmaPoly.reduceform(apoly) [source] this assumes no exog, todo

statsmodels.tsa.varma_process.VarmaPoly.hstackarma_minus1 VarmaPoly.hstackarma_minus1() [source] stack ar and lagpolynomial vertically in 2d array this is the Kalman Filter representation, I think

statsmodels.tsa.varma_process.VarmaPoly.hstack VarmaPoly.hstack(a=None, name='ar') [source] stack lagpolynomial horizontally in 2d array

statsmodels.tsa.varma_process.VarmaPoly.getisstationary VarmaPoly.getisstationary(a=None) [source] check whether the auto-regressive lag-polynomial is stationary Returns: isstationary : boolean *attaches* : areigenvalues : complex array eigenvalues sorted by absolute value References formula taken from NAG manual

statsmodels.tsa.varma_process.VarmaPoly.getisinvertible VarmaPoly.getisinvertible(a=None) [source] check whether the auto-regressive lag-polynomial is stationary Returns: isinvertible : boolean *attaches* : maeigenvalues : complex array eigenvalues sorted by absolute value References formula taken from NAG manual

statsmodels.tsa.vector_ar.var_model.VAR.select_order VAR.select_order(maxlags=None, verbose=True) [source] Compute lag order selections based on each of the available information criteria Parameters: maxlags : int if None, defaults to 12 * (nobs/100.)**(1./4) verbose : bool, default True If True, print table of info criteria and selected orders Returns: selections : dict {info_crit -> selected_order}

statsmodels.tsa.vector_ar.var_model.VAR.score VAR.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.tsa.vector_ar.var_model.VAR.predict VAR.predict(params, start=None, end=None, lags=1, trend='c') [source] Returns in-sample predictions or forecasts

statsmodels.tsa.vector_ar.var_model.VAR.loglike VAR.loglike(params) Log-likelihood of model.