Dias, G.F. and Kapetanios, G. (2018) Estimation and forecasting in vector autoregressive moving average models for rich datasets. Journal of Econometrics, 202 (1). pp. 75-91. ISSN 0304-4076
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Elsevier. This is an author-produced version of a paper subsequently published in Journal of Econometrics. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | VARMA; Weak VARM; AIterative ordinary least squares (IOLS) estimator; Asymptotic contraction mapping; Forecasting; Rich and large datasets |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Economics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Apr 2019 15:52 |
Last Modified: | 24 Aug 2019 00:42 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.jeconom.2017.06.022 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:144638 |