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Heuristic model selection for leading indicators in Russia and Germany

  • Ivan Savin
  • Peter Winker

Business tendency survey indicators are widely recognised 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 countries revealing marked differences between Russia and Germany. JEL classification: C52, C61, E37 Keywords: Leading indicators, business cycle forecasts, VAR, model selection, genetic algorithms

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File URL: http://dx.doi.org/10.1787/jbcma-2012-5k49pkpbf76j
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Article provided by OECD Publishing,Centre for International Research on Economic Tendency Surveys in its journal OECD Journal: Journal of Business Cycle Measurement and Analysis.

Volume (Year): 2012 (2012)
Issue (Month): 2 ()
Pages: 67-89

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Handle: RePEc:oec:stdkab:5k49pkpbf76j
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