Optimizing time-series forecasts for inflation and interest rates using simulation and model averaging

Jumah, Adusei and Kunst, Robert M. (2016) Optimizing time-series forecasts for inflation and interest rates using simulation and model averaging. Applied Economics, 48 (45), pp. 4366-4378.

Full text not available from this repository.

Abstract or Table of Contents

Motivated by economic-theory concepts – the Fisher hypothesis and the theory of the term structure – we consider a small set of simple bivariate closed-loop time-series models for the prediction of price inflation and of long- and short-term interest rates. The set includes vector autoregressions (VAR) in levels and in differences, a cointegrated VAR and a non-linear VAR with threshold cointegration based on data from Germany, Japan, UK and the US. Following a traditional comparative evaluation of predictive accuracy, we subject all structures to a mutual validation using parametric bootstrapping. Ultimately, we utilize the recently developed technique of Mallows model averaging to explore the potential of improving upon the predictions through combinations. While the simulations confirm the traded wisdom that VARs in differences optimize one-step prediction and that error correction helps at larger horizons, the model-averaging experiments point at problems in allotting an adequate penalty for the complexity of candidate models. (Author's abstract)

Item Type: Article in Academic Journal
Keywords: Threshold cointegration, parametric bootstrap, model averaging, interest rates
Classification Codes (e.g. JEL): JEL: C32, C52, E43, E47
Research Groups: Macroeconomics and Public Finance
Status: Published
Date Deposited: 10 May 2017 13:11
Last Modified: 10 May 2017 13:11
URI: http://irihs.ihs.ac.at/id/eprint/4274

Actions (login required)

View Item View Item