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政大典藏 > College of Social Science > Department of Economics > Periodical Articles > Item 140.119/139778

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/139778

Title:	Optimal Multi-step VAR Forecast Averaging
Authors:	廖仁哲 Liao , Jen-Che Tsay, Wen-Jen
Contributors:	經濟系
Date:	2020-12
Issue Date:	2022-04-11 13:24:14 (UTC+8)
Abstract:	This article proposes frequentist multiple-equation least-squares averaging approaches for multistep 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 multistep forecast averaging, respectively. Under the framework of stationary VAR processes of infinite 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 multistep 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.
Relation:	Econometric Theory, Vol.36, No.6, pp.1099-1126
Data Type:	article
DOI link:	https://doi.org/10.1017/S0266466619000434
DOI:	10.1017/S0266466619000434
Appears in Collections:	[Department of Economics] Periodical Articles

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