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

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...

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Bibliographic Details
Published in:Journal of econometrics 2018-01, Vol.202 (1), p.75-91
Main Authors: Dias, Gustavo Fruet, Kapetanios, George
Format: Article
Language:English
Subjects:
Asymptotic contraction mapping
Asymptotic methods
Bayesian analysis
Economic models
Forecasting
Iterative ordinary least squares (IOLS) estimator
Monte Carlo simulation
Regression analysis
Rich and large datasets
Studies
VARMA
Weak VARMA
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Summary: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.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2017.06.022