Journalartikel

Jahr der Veröffentlichung: 2012

Seiten: 67-89

Zeitschrift: OECD Journal: Journal of Business Cycle Measurement and Analysis

Bandnummer: 2012

Heftnummer: 2

DOI Link: https://doi.org/10.1787/jbcma-2012-5k49pkpbf76j

Verlag: OECD Publishing

Abstract: 

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.

Harvard-Zitierstil: Savin, I. and Winker, P. (2012) Heuristic Model Selection for Leading Indicators in Russia and Germany, OECD Journal: Journal of Business Cycle Measurement and Analysis, 2012(2), pp. 67-89. https://doi.org/10.1787/jbcma-2012-5k49pkpbf76j

APA-Zitierstil: Savin, I., & Winker, P. (2012). Heuristic Model Selection for Leading Indicators in Russia and Germany. OECD Journal: Journal of Business Cycle Measurement and Analysis. 2012(2), 67-89. https://doi.org/10.1787/jbcma-2012-5k49pkpbf76j