Bayesian nonparametric vector autoregressive models

Kalli, Maria and Griffin, Jim E. (2018) Bayesian nonparametric vector autoregressive models. Journal of Econometrics, 203 (2). pp. 267-282. ISSN 0304-4076. (doi:https://doi.org/10.1016/j.jeconom.2017.11.009) (Full text available)

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https://doi.org/10.1016/j.jeconom.2017.11.009

Abstract

Vector autoregressive (VAR) models are the main work-horse model for macroeconomic forecasting, and provide a framework for the analysis of complex dynamics that are present between macroeconomic variables. Whether a classical or a Bayesian approach is adopted, most VAR models are linear with Gaussian innovations. This can limit the model’s ability to explain the relationships in macroeconomic series. We propose a nonparametric VAR model that allows for nonlinearity in the conditional mean, heteroscedasticity in the conditional variance, and non-Gaussian innovations. Our approach differs to that of previous studies by modelling the stationary and transition densities using Bayesian nonparametric methods. Our Bayesian nonparametric VAR (BayesNP-VAR) model is applied to US and UK macroeconomic time series, and compared to other Bayesian VAR models. We show that BayesNP-VAR is a flexible model that is able to account for nonlinear relationships as well as heteroscedas- ticity in the data. In terms of short-run out-of-sample forecasts, we show that BayesNP-VAR predictively outperforms competing models.

Item Type: Article
Uncontrolled keywords: Vector Autoregressive Models; Dirichlet Process Prior; Infinite Mixtures; Markov chain Monte Carlo
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Depositing User: Maria Kalli
Date Deposited: 25 Jan 2018 15:25 UTC
Last Modified: 18 Oct 2018 12:13 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/65792 (The current URI for this page, for reference purposes)
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