VAR forecasting under misspecification

The paper considers multi-step forecasting of a stationary vector process under a quadratic loss function with a collection of finite-order vector autoregressions (VAR). Under severe misspecification it is preferable to use the multi-step loss function also for parameter estimation. We propose a modification to Shibataʹs (Ann. Statist. 8 (1980) 147) final prediction error criterion to jointly choose the VAR lag order and one of two predictors: the maximum likelihood estimator plug-in predictor or the loss function estimator plug-in predictor. A Monte Carlo experiment illustrates the theoretical results and documents the empirical performance of the selection criterion.