Achieving Shrinkage in a Time-Varying Parameter Model Framework

TL;DR

This paper introduces a Bayesian shrinkage approach for time-varying parameter models that automatically reduces parameters to static ones to prevent overfitting, using a double gamma prior and an efficient MCMC scheme.

Contribution

It develops a novel Bayesian shrinkage method with an efficient MCMC algorithm for TVP models, applicable to univariate and multivariate time series, enhancing model flexibility and robustness.

Findings

Effective shrinkage to static parameters demonstrated

Applicable to both univariate and multivariate models

Improved inflation and volatility modeling results

Abstract

Shrinkage for time-varying parameter (TVP) models is investigated within a Bayesian framework, with the aim to automatically reduce time-varying parameters to static ones, if the model is overfitting. This is achieved through placing the double gamma shrinkage prior on the process variances. An efficient Markov chain Monte Carlo scheme is developed, exploiting boosting based on the ancillarity-sufficiency interweaving strategy. The method is applicable both to TVP models for univariate as well as multivariate time series. Applications include a TVP generalized Phillips curve for EU area inflation modelling and a multivariate TVP Cholesky stochastic volatility model for joint modelling of the returns from the DAX-30 index.