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Heuristic model selection for leading indicators in Russia and Germany

Ivan Savin and Peter Winker ()

No 46, Working Papers from COMISEF

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

Keywords: Leading indicators; business cycle forecasts; VAR; model selection; genetic algorithms. (search for similar items in EconPapers)
Pages: 30 pages
Date: 2011-01-27
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-for
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Citations: View citations in EconPapers (2) Track citations by RSS feed

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Related works:
Journal Article: Heuristic model selection for leading indicators in Russia and Germany (2013) Downloads
Working Paper: Heuristic model selection for leading indicators in Russia and Germany (2011) Downloads
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