statsmodels.tsa.vector_ar.var_model.VARResults.resid static VARResults.resid() [source] Residuals of response variable resulting from estimated coefficients

statsmodels.tsa.vector_ar.var_model.VARResults.pvalues static VARResults.pvalues() [source] Two-sided p-values for model coefficients from Student t-distribution

statsmodels.tsa.vector_ar.var_model.VARResults.llf static VARResults.llf() [source] Compute VAR(p) loglikelihood

statsmodels.tsa.vector_ar.var_model.VARResults.info_criteria static VARResults.info_criteria() [source] information criteria for lagorder selection

statsmodels.tsa.vector_ar.var_model.VARResults.fittedvalues static VARResults.fittedvalues() [source] The predicted insample values of the response variables of the model.

statsmodels.tsa.vector_ar.var_model.VARResults.detomega static VARResults.detomega() [source] Return determinant of white noise covariance with degrees of freedom correction:

statsmodels.tsa.vector_ar.var_model.VARResults.cov_params static VARResults.cov_params() [source] Estimated variance-covariance of model coefficients Notes Covariance of vec(B), where B is the matrix [intercept, A_1, ..., A_p] (K x (Kp + 1)) Adjusted to be an unbiased estimator Ref: Lutkepohl p.74-75

statsmodels.tsa.vector_ar.var_model.VARResults.bse static VARResults.bse() Standard errors of coefficients, reshaped to match in size

statsmodels.robust.robust_linear_model.RLMResults.weights static RLMResults.weights() [source]

statsmodels.robust.robust_linear_model.RLMResults.tvalues static RLMResults.tvalues() Return the t-statistic for a given parameter estimate.