Forecasting Exchange Rate in a Large Bayesian VAR Model: The Case of Taiwan

Abstract

We study the out-of-sample forecasting performance of 32 exchange rates vis-a-vis theNew Taiwan Dollar (NTD) in a 32-variable vector autoregression (VAR) model. TheBayesian approach is applied to the large-scale VAR model (LBVAR), and its (time-varying) forecasting performance is compared to the random-walk model in terms of bothforecast accuracy and Giacomini-Rossi fluctuation tests. We find the random-walk modeloutperforms the LBVAR model in a short-run forecasting competition. Moreover, thedominance of a random-walk in the competition is stable over time. Accordingly, we donot find any benefit of incorporating a rich set of information in predicting the exchangerates vis-a-vis the NTD.

Keywords:

• Bayesian Approach,
• Forecast Stability,
• Vector Autoregression