Bayesian nonparametric vector autoregressive models

By Maria Kalli, Jim E. Griffin. 2018

61 citations recorded by the source corpus.

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. 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 from 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 heteroscedasticity in the data. In terms of short-run out-of-sample forecasts, we show that BayesNP-VAR predictively outperforms competing models.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7d744792b73889a59f31b8358277256281e95976

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In Economics and Econometrics

In Management Science and Operations Research

Citations in the corpus

Bayesian forecasting in economics and finance: A modern review (2022, background): “, 2021) or infinite mixtures (Kalli and Griffin, 2018), allow the researcher to uncover such unknown functional forms and produce precise macroeconomic forecasts.”