Research Highlights

Optimal Multi-step VAR Forecast Averaging (with Jen-Che Liao, published in ECONOMETRIC THEORY)

2020-12-29

Author Jen-Che Liao
Wen-Jen Tsay
Abstract This paper proposes frequentist multiple-equation least squares averaging approaches for multi-step forecasting with vector autoregressive (VAR) models. The proposed VAR forecast averaging methods are based on the multivariate Mallows model averaging (MMMA) and multivariate leave-h-out cross-validation averaging (MCVAh) criteria (with h denoting the forecast horizon), which are valid for iterative and direct multi-step forecast averaging, respectively. Under the framework of stationary VAR processes of in finite order, we provide theoretical justifications by establishing asymptotic unbiasedness and asymptotic optimality of the proposed forecast averaging approaches. Specifically, MMMA exhibits asymptotic optimality for one-step-ahead forecast averaging, whereas for direct multi-step forecast averaging, the asymptotically optimal combination weights are determined separately for each forecast horizon based on the MCVAh procedure. To present our methodology, we investigate the finite-sample behavior of the proposed averaging procedures under model misspecification via simulation experiments.
Link https://www.cambridge.org/core/journals/econometric-theory/article/optimal-multistep-var-forecast-averaging/7E47C08E9ED3CC1AA329AD9CA5638045(Open New Window)