Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets

40 Pages Posted: 23 Dec 2015 Last revised: 11 Jun 2021

See all articles by Gustavo Fruet Dias

Gustavo Fruet Dias

University of East Anglia (UEA) - School of Economics; CREATES

George Kapetanios

King's College, London

Date Written: May 29, 2017

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.

Supplement is available at: https://ssrn.com/abstract=2830838

Keywords: VARMA, weak VARMA, weak ARMA, Forecasting, Large datasets, Iterative ordinary least squares (IOLS) estimator, Asymptotic contraction mapping

JEL Classification: C13, C32, C53, C63, E0

Suggested Citation

Fruet Dias, Gustavo and Kapetanios, George, Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets (May 29, 2017). Available at SSRN: https://ssrn.com/abstract=2707176 or http://dx.doi.org/10.2139/ssrn.2707176

Gustavo Fruet Dias (Contact Author)

University of East Anglia (UEA) - School of Economics ( email )

3.06, Registry
University of East Anglia
Norwich, NR4 7TJ
United Kingdom

CREATES

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

George Kapetanios

King's College, London ( email )

30 Aldwych
London, WC2B 4BG
United Kingdom
+44 20 78484951 (Phone)

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