Title: A quasi-Bayesian nonparametric approach to time varying parameter VAR models
Authors: Katerina Petrova - Queen Mary University London (United Kingdom) [presenting]
Abstract: A quasi-Bayesian local likelihood (QBLL) estimation methodology is established for a multivariate model with time varying parameters. The validity of the resulting quasi-posterior distributions of the drifting parameters is proven in general and, in the special case of a Gaussian VAR model, a closed form Normal-Wishart expression for the quasi-posterior distribution of the QBLL estimator is provided. In addition, several Gibbs algorithms are developed, which can sample from a VAR model with a mixture of time varying and time invariant parameters. The proposed estimators differ from existing state space approaches to VAR models in that they estimate parameter time variation nonparametrically without imposing assumptions on the stochastic processes of the parameters. The QBLL estimators exhibit good finite sample properties and their performance compares well to existing parametric state space models, as illustrated by a Monte Carlo exercise. In addition, we demonstrate that the QBLL approach provides a remedy to the `curse of dimensionality' by accommodating large dimensional VAR systems and delivers improvements in the out-of-sample forecasts of key macroeconomic variables. Finally, an empirical contribution to the literature on changing macro dynamics in the US is made, presenting evidence of a fall in inflation persistence and volatility during the Great Moderation period, in line with previous results.