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Bayesian nonparametric time varying vector autoregressive models

Kalli, Maria, Griffin, Jim (2019) Bayesian nonparametric time varying vector autoregressive models. Journal of Business and Economic Statistics, . ISSN 1537-2707. (In press) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)

Abstract

Vector autoregressive (VAR) models are the main work-horse models for macroeconomic forecasting, and provide a framework for the analysis of complex dynamics that are present between macroeconomic variables. However, 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 time-dependent nonparametric VAR model that allows for nonlinearity in the conditional mean, heteroscedasticity in the conditional variance, non-Gaussian innovations, and time-dependent transion density. The novel construction of the transion density allows for the identification of regime changes, informed by the lagged values of the variables in the systems and the time dependence. Our approach differs from that of previous studies by modelling the stationary and transition densities using Bayesian nonparametric methods. Our Bayesian nonparametric time varying VAR (BayesNP-TV-VAR) model is applied to US and UK macroeconomic time series, and compared to the gold standard TVP-SV-VAR. We show that BayesNP-TV-VAR is a flexible model that is able to account for nonlinear relationships, time-varying parameters, as well as heteroscedasticity in the data. In terms of both short-term and long term out-of-sample forecasts, we show that BayesNP-TV-VAR predictively outperforms the TVP-SV-VAR.

Item Type: Article
Uncontrolled keywords: Vector autoregressive models, Dirichlet process prior, Infinite mixtures, Compound Random Measures, Markov chain Monte Carlo, Statistics
Subjects: Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Maria Kalli
Date Deposited: 13 Nov 2019 12:52 UTC
Last Modified: 06 Feb 2020 16:07 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/78656 (The current URI for this page, for reference purposes)
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