Estimating TVP-VAR models with time invariant long-run multipliers

TL;DR

This paper introduces a Gibbs sampling method for estimating TVP-VAR models with fixed long-run relationships, improving forecast accuracy in economic data analysis.

Contribution

It develops a novel estimation approach that incorporates time-invariant long-run multipliers into TVP-VAR models, enhancing their forecasting performance.

Findings

Incorporating long-run invariance improves forecasts

Method applied to Norwegian and Russian economies

Significant forecast accuracy gains observed

Abstract

The main goal of this paper is to develop a methodology for estimating time varying parameter vector auto-regression (TVP-VAR) models with a timeinvariant long-run relationship between endogenous variables and changes in exogenous variables. We propose a Gibbs sampling scheme for estimation of model parameters as well as time-invariant long-run multiplier parameters. Further we demonstrate the applicability of the proposed method by analyzing examples of the Norwegian and Russian economies based on the data on real GDP, real exchange rate and real oil prices. Our results show that incorporating the time invariance constraint on the long-run multipliers in TVP-VAR model helps to significantly improve the forecasting performance.