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

Gustavo Fruet Dias, George Kapetanios

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
21 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)75-91
Number of pages17
JournalJournal of Econometrics
Issue number1
Early online date24 Aug 2017
Publication statusPublished - 1 Jan 2018


  • Weak VARMA
  • Iterative ordinary least squares (IOLS) estimator
  • Asymptotic contraction mapping
  • Forecasting
  • Rich and large datasets

Cite this