Estimation and forecasting in vector autoregressive moving average models for rich datasets

Fruet Dias, Gustavo ORCID: https://orcid.org/0000-0001-6428-5074 and Kapetanios, George (2018) Estimation and forecasting in vector autoregressive moving average models for rich datasets. Journal of Econometrics, 202 (1). pp. 75-91. ISSN 0304-4076

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Abstract

We address the issue of modelling and forecasting macroeconomic variables using rich datasets by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (IOLS) estimator. We establish the consistency and asymptotic distribution of the estimator for weak and strong VARMA(p,q) models. Monte Carlo results show that IOLS is consistent and feasible for large systems, outperforming the MLE and other linear regression based efficient estimators under alternative scenarios. Our empirical application shows that VARMA models are feasible alternatives when forecasting with many predictors. We show that VARMA models outperform the AR(1), ARMA(1,1), Bayesian VAR, and factor models, considering different model dimensions.

Item Type:	Article
Uncontrolled Keywords:	varma,weak varma,iterative ordinary least squares (iols) estimator,asymptotic contraction mapping,forecasting,rich and large datasets
Faculty \ School:	Faculty of Social Sciences > School of Economics
UEA Research Groups:	Faculty of Social Sciences > Research Groups > Applied Econometrics And Finance Faculty of Social Sciences > Research Groups > Industrial Economics
Depositing User:	LivePure Connector
Date Deposited:	30 Sep 2019 09:30
Last Modified:	14 May 2023 00:07
URI:	https://ueaeprints.uea.ac.uk/id/eprint/72417
DOI:	10.1016/j.jeconom.2017.06.022

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