Working paper/research report
Authors list: Savin, I.; Winker, P.
Publication year: 2011
URL: https://www.uni-giessen.de/static_files/pcms/jlu/comisef/files/wps046.pdf
Title of series: COMISEF Working Papers Series
Number in series: WPS-046
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 countries revealing marked differences between Russia and Germany.
Abstract:
Citation Styles
Harvard Citation style: Savin, I. and Winker, P. (2011) Heuristic model selection for leading indicators in Russia and Germany. (COMISEF Working Papers Series, WPS-046). https://www.uni-giessen.de/static_files/pcms/jlu/comisef/files/wps046.pdf
APA Citation style: Savin, I., & Winker, P. (2011). Heuristic model selection for leading indicators in Russia and Germany. (COMISEF Working Papers Series, WPS-046). https://www.uni-giessen.de/static_files/pcms/jlu/comisef/files/wps046.pdf
