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Forecasting the Swiss economy using VECX models: An exercise in forecast combination across models and observation windows

Published online by Cambridge University Press:  26 March 2020

Katrin Assenmacher-Wesche  and
M. Hashem Pesaran
Katrin Assenmacher-Wesche*
Affiliation:
Swiss National Bank
M. Hashem Pesaran
Affiliation:
Cambridge University
*
e-mail: katrin.assenmacher-wesche@snb.ch
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Abstract

This paper uses vector error correction models of Switzerland for forecasting output, inflation and the short-term interest rate. It considers three different ways of dealing with forecast uncertainties. First, it investigates the effect on forecasting performance of averaging over forecasts from different models. Second, it considers averaging forecasts from different estimation windows. It is found that averaging over estimation windows is at least as effective as averaging over different models and both complement each other. Third, it examines whether using weighting schemes from the machine learning literature improves the average forecast. Compared to equal weights the effect of alternative weighting schemes on forecast accuracy is small in the present application.

Keywords

Bayesian model averagingchoice of observation windowlong-run structural vector autoregressionC53C32
Type
Articles
Information
National Institute Economic Review , Volume 203 , January 2008 , pp. 91 - 108
Copyright
Copyright © 2008 National Institute of Economic and Social Research

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