statsmodels.tsa.vector_ar.svar_model.SVARResults.forecast_cov#
- SVARResults.forecast_cov(steps=1, method='mse')#
Compute forecast covariance matrices for desired number of steps
- Parameters:
- steps
int Number of steps ahead to compute forecast covariances for.
- method{“mse”, “auto”},
default“mse” If “mse”, use the forecast MSE, ignoring parameter uncertainty. If “auto”, also take parameter uncertainty into account by adding the forecast error covariance due to parameter uncertainty; this is currently only supported if there is no exogenous data and the trend is one of “n” or “c”.
- steps
- Returns:
- covs
ndarray(stepsxkxk)
- covs
Notes
\[\Sigma_{\hat y}(h) = \Sigma_y(h) + \Omega(h) / T\]Ref: Lütkepohl pp. 96-97