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

Gustavo Fruet Dias and George Kapetanios

Journal of Econometrics, 2018, vol. 202, issue 1, 75-91

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.

Keywords: VARMA; Weak VARMA; Iterative ordinary least squares (IOLS) estimator; Asymptotic contraction mapping; Forecasting; Rich and large datasets (search for similar items in EconPapers)
JEL-codes: C13 C32 C53 C63 E0 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:202:y:2018:i:1:p:75-91

DOI: 10.1016/j.jeconom.2017.06.022

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