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Forecasting the Swiss Economy Using Vecx* Models: an Exercise in Forecast Combination Across Models and Observation Windows

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Author Info

  • Katrin Assenmacher-Wesche

    (Swiss National Bank, e-mail: katrin.assenmacher-wesche@snb.ch)

  • M. Hashem Pesaran

    (Cambridge University, CIMF, and USC)

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.

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Bibliographic Info

Article provided by National Institute of Economic and Social Research in its journal National Institute Economic Review.

Volume (Year): 203 (2008)
Issue (Month): 1 (January)
Pages: 91-108

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Handle: RePEc:sae:niesru:v:203:y:2008:i:1:p:91-108

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Related research

Keywords: Bayesian model averaging; choice of observation window; long-run structural vector autoregression;

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  1. Assenmacher-Wesche, K. & Pesaran, M.H., 2008. "A VECX* Model of the Swiss Economy," Cambridge Working Papers in Economics 0809, Faculty of Economics, University of Cambridge.
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Cited by:
  1. Pesaran, M.H. & Schuermann, T. & Smit, L.V., 2008. "Forecasting Economic and Financial Variables with Global VARs," Cambridge Working Papers in Economics 0807, Faculty of Economics, University of Cambridge.
  2. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
  3. M. Hashem Pesaran & Andreas Pick, 2009. "Forecasting Random Walks under Drift Instability," DNB Working Papers 207, Netherlands Central Bank, Research Department.
  4. Katrin Assenmacher-Wesche & M. Hashem Pesaran, 2009. "A VECX* model of the Swiss economy," Economic Studies 2009-06, Swiss National Bank.
  5. Prasad S Bhattacharya & Dimitrios D Thomakos, 2011. "Improving forecasting performance by window and model averaging," CAMA Working Papers 2011-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

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