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Heuristic model selection for leading indicators in Russia and Germany

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  • Ivan Savin
  • Peter Winker

Abstract

Business tendency survey indicators are widely recognized as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare full–specified VAR models with subset models obtained using a Genetic Algorithm enabling ’holes’ in multivariate lag structures. The problem is complicated by the fact that a structural break and seasonal variation of indicators have to be taken into account. The models allow for a comparison of the dynamic adjustment and the forecasting performance of the leading indicators for both

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Bibliographic Info

Paper provided by COMISEF in its series Working Papers with number 046.

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Length: 30 pages
Date of creation: 27 Jan 2011
Date of revision:
Handle: RePEc:com:wpaper:046

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Web page: http://www.comisef.eu

Related research

Keywords: Leading indicators; business cycle forecasts; VAR; model selection; genetic algorithms.;

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Cited by:
  1. Anna Staszewska-Bystrova, 2013. "Modified Scheffé’s Prediction Bands," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 233(5-6), pages 680-690, October.
  2. Roberto Baragona & Domenico Cucina, 2013. "Multivariate Self-Exciting Threshold Autoregressive Modeling by Genetic Algorithms," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 233(1), pages 3-21, January.

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