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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: [email protected]
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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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References

Assenmacher-Wesche, K. and Pesaran, M.H. (2008), A VECX∗ model of the Swiss economy’, Swiss National Bank Economy Studies (forthcoming).Google Scholar
Clark, T.E. and McCracken, M.W. (2004), ‘Improving forecast accuracy by combining recursive and rolling forecasts’, Federal Reserve Bank of Kansas City, mimeo.Google Scholar
Clark, T.E. and McCracken, M.W. (2006), ‘Averaging forecasts for VARs with uncertain instabilities’, Federal Reserve Bank of Kansas City, Research Working Paper No. RWP-06-12.Google Scholar
Clements, M.P. and Hendry, D.F. (1998), Forecasting Economic Time Series, Cambridge, Cambridge University Press.CrossRefGoogle Scholar
Clements, M.P. and Hendry, D.F. (1999), Forecasting Non-Stationary Economic Time Series, Cambridge (Mass.), MIT Press.Google Scholar
Clements, M.P. and Hendry, D.F. (2006), ‘Forecasting with breaks’, in Elliot, G., Granger, C.W.J. and Timmermann, A. (eds), Handbook of Economic Forecasting, Amsterdam, Elsevier, pp. 605–57.Google Scholar
Deschamps, P.J. (2007), ‘Comparing smooth transition and Markov switching autoregressive models of US unemployment’, Université de Fribourg, mimeo.Google Scholar
Diebold, F.X. and Mariano, R.S. (1995), ‘Comparing predictive accuracy’, Journal of Business and Economic Statistics, 13, pp. 134–44.Google Scholar
Elliot, G. and Timmermann, A. (2007), ‘Economic forecasting’, CEPR Discussion Paper 6158.Google Scholar
Garratt, A., Lee, K., Pesaran, M.H. and Shin, Y. (2003a), ‘A long run structural macroeconometric model of the UK’, Economic Journal, 113, pp. 412–55.CrossRefGoogle Scholar
Garratt, A., Lee, K., Pesaran, M.H. and Shin, Y. (2003b), ‘Forecast uncertainties in macroeconomic modeling: an application to the UK economy’, Journal of the American Statistical Association, 98, pp. 829–38.CrossRefGoogle Scholar
Garratt, A., Lee, K., Pesaran, M.H. and Shin, Y. (2006), Global and National Macroeconometric Modelling: A Long Run Structural Approach, Oxford, Oxford University Press.CrossRefGoogle Scholar
Geweke, J. and Whiteman, C.H. (2006), ‘Bayesian forecasting’, in Elliot, G., Granger, C.W.J. and Timmermann, A. (eds), Handbook of Economic Forecasting, Amsterdam, Elsevier, pp. 3–80.CrossRefGoogle Scholar
Harvey, D., Leybourne, S.J. and Newbold, P. (1997), ‘Testing the equality of prediction mean squared errors’, International Journal of Forecasting, 13, pp. 281 -91.CrossRefGoogle Scholar
Jordan, T.J. and Savioz, M.R. (2003), ‘Does it make sense to combine forecasts from VAR models? An empirical analysis with inflation forecasts for Switzerland’, Schweizerische Nationalbank, Quartalsheft, 4, pp. 80–93.Google Scholar
Pesaran, M.H., Pettenuzzo, D. and Timmermann, A. (2006), ‘Forecasting time series subject to multiple structural breaks’, Review of Economic Studies, 73, pp. 1057–84.Google Scholar
Pesaran, M.H. and Pick, A. (2007), ‘Forecasting random walk models under drift instability’, University of Cambridge, mimeo.CrossRefGoogle Scholar
Pesaran, M.H., Schleicher, C. and Zaffaroni, P. (2007), ‘Model averaging in risk management with an application to futures markets’, mimeo.Google Scholar
Pesaran, M.H. and Skouras, S. (2002), ‘Decision-based methods for forecast evaluation’, in Clements, M.P. and Hendry, D.F. (eds), A Companion to Economic Forecasting, Oxford, Basil Blackwell, pp. 241–67.Google Scholar
Pesaran, M.H. and Timmermann, A. (2007), ‘Selection of estimation window in the presence of breaks’, Journal of Econometrics, 137, pp. 134–61.CrossRefGoogle Scholar
Sancetta, A. (2006), ‘Online forecast combination for dependent heterogeneous data’, University of Cambridge, mimeo.Google Scholar
Smith, J. and Wallis, K.F. (2007), ‘A simple explanation of the forecast combination puzzle’, University of Warwick, mimeo.Google Scholar
Timmermann, A. (2006), ‘Forecast combinations’, in Elliot, G., Granger, C.W.J. and Timmermann, A. (eds), Handbook of Economic Forecasting, Amsterdam, Elsevier, pp. 135–96.Google Scholar
Yang, Y. (2004), ‘Combining forecasting procedures: some theoretical results’, Econometric Theory, 20, pp. 176–222.CrossRefGoogle Scholar