statsmodels.tsa.vector_ar.var_model.VARResults.resid_acorr VARResults.resid_acorr(nlags=1) [source] Compute sample autocorrelation (including lag 0) Parameters: nlags : int

statsmodels.tsa.vector_ar.var_model.VARResults.reorder VARResults.reorder(order) [source] Reorder variables for structural specification

statsmodels.tsa.vector_ar.var_model.VARResults.plot_sample_acorr VARResults.plot_sample_acorr(nlags=10, linewidth=8) [source] Plot theoretical autocorrelation function

statsmodels.tsa.vector_ar.var_model.VARResults.plot_forecast VARResults.plot_forecast(steps, alpha=0.05, plot_stderr=True) [source] Plot forecast

statsmodels.tsa.vector_ar.var_model.VARResults.plot_acorr VARResults.plot_acorr(nlags=10, linewidth=8) Plot theoretical autocorrelation function

statsmodels.tsa.vector_ar.var_model.VARResults.plotsim VARResults.plotsim(steps=1000) Plot a simulation from the VAR(p) process for the desired number of steps

statsmodels.tsa.vector_ar.var_model.VARResults.plot VARResults.plot() [source] Plot input time series

statsmodels.tsa.vector_ar.var_model.VARResults.orth_ma_rep VARResults.orth_ma_rep(maxn=10, P=None) Compute Orthogonalized MA coefficient matrices using P matrix such that . P defaults to the Cholesky decomposition of Parameters: maxn : int Number of coefficient matrices to compute P : ndarray (k x k), optional Matrix such that Sigma_u = PP?, defaults to Cholesky descomp Returns: coefs : ndarray (maxn x k x k)

statsmodels.tsa.vector_ar.var_model.VARResults.mse VARResults.mse(steps) Compute theoretical forecast error variance matrices Parameters: steps : int Number of steps ahead Returns: forc_covs : ndarray (steps x neqs x neqs) Notes

statsmodels.tsa.vector_ar.var_model.VARResults.mean VARResults.mean() Mean of stable process Lutkepohl eq. 2.1.23