Template-Type: ReDIF-Paper 1.0
Author-Name: Florian HUber
Author-Name-First: Florian
Author-Name-Last: HUber
Author-Workplace-Name: University of Salzburg
Author-Name: Gary Koop
Author-Name-First: Gary
Author-Name-Last: Koop
Author-Workplace-Name: University of Strathclyde
Author-Name: Michael Pfarrhoffer
Author-Name-First: Michael
Author-Name-Last: Pfarrhoffer
Author-Workplace-Name: University of Salzburg
Title: Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations
Abstract: Researchers increasingly wish to estimate time-varying parameter (TVP) regressions
which involve a large number of explanatory variables. Including
prior information to mitigate over-parameterization concerns has led to many
using Bayesian methods. However, Bayesian Markov Chain Monte Carlo
(MCMC) methods can be very computationally demanding. In this paper,
we develop computationally efficient Bayesian methods for estimating TVP
models using an integrated rotated Gaussian approximation (IRGA). This
exploits the fact that whereas constant coefficients on regressors are often
important, most of the TVPs are often unimportant. Since Gaussian distributions
are invariant to rotations we can split the the posterior into two
parts: one involving the constant coefficients, the other involving the TVPs.
Approximate methods are used on the latter and, conditional on these, the
former are estimated with precision using MCMC methods. In empirical exercises
involving artificial data and a large macroeconomic data set, we show
the accuracy and computational benets of IRGA methods. 
Length:  pages
Creation-Date: 
Revision-Date: 
Publication-Status: 
Number: 2304
Classification-JEL: C11, C30, E3, D31
Keywords: Time-varying, parameter regression, Bayesian, Gaussian approximation, macroeconomic forecasting
Handle: RePEc:str:wpaper:2304