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

Gustavo Fruet Dias () and George Kapetanios
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Gustavo Fruet Dias: Aarhus University and CREATES, Postal: Department of Economics and Business, Fuglesangs Allé 4, 8210 Aarhus V, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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 strong and weak 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), BVAR and factor models, considering different model dimensions.

Keywords: VARMA; weak VARMA; weak ARMA; Forecasting; Rich and Large datasets; Iterative ordinary least squares (IOLS) estimator; Asymptotic contraction mapping. (search for similar items in EconPapers)
JEL-codes: C13 C32 C53 C63 E0 (search for similar items in EconPapers)
Pages: 100
Date: 2014-10-23
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Estimation and forecasting in vector autoregressive moving average models for rich datasets (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2014-37

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