Template-type: ReDIF-Paper 1.0
Author-Name: Katrin Assenmacher
Author-Name-First: Katrin
Author-Name-Last: Assenmacher
Author-Person: pwe101
Author-Name: M. Hashem Pesaran
Author-Name-First: M. Hashem
Author-Name-Last: Pesaran
Author-Person: ppe34
Title: Forecasting the Swiss Economy Using VECX* Models: An Exercise in Forecast Combination Across Models and Observation Windows
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.
Length: 44 pages
Creation-Date: 2008
Contact-Email: forschung@snb.ch
File-URL: https://www.snb.ch/en/publications/research/working-papers/2008/working_paper_2008_03
File-Format: text/html
Number: 2008-03
Classification-JEL: C53, C32
Keywords: Bayesian model averaging, choice of observation window, long-run structural vector autoregression
Handle: RePEc:snb:snbwpa:2008-03