Heuristic model selection for leading indicators in Russia and Germany
Heuristic model selection for leading indicators in Russia and Germany:

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 es...

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Gespeichert in:
Bibliographische Detailangaben
1. Verfasser: Savin, Ivan (VerfasserIn)
Weitere Verfasser: Winker, Peter (MitwirkendeR)
Format: Elektronisch Buchkapitel
Sprache:English
Veröffentlicht: Paris OECD Publishing 2013
Schlagworte:
Economics
Online-Zugang:DE-384
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URL des Erstveröffentlichers
Zusammenfassung: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
Beschreibung:1 Online-Ressource (23 Seiten) 21 x 28cm
DOI:10.1787/jbcma-2012-5k49pkpbf76j

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