Template-Type: ReDIF-Paper 1.0 Series: Tinbergen Institute Discussion Papers Creation-Date: 2011-08-22 Number: 11-123/4 Author-Name: Monica Billio Author-Workplace-Name: University of Venice, Gretta Assoc. and School for Advanced Studies In Venice Author-Name: Roberto Casarin Author-Workplace-Name: University of Venice, Gretta Assoc. and School for Advanced Studies In Venice Author-Name: Francesco Ravazzolo Author-Workplace-Name: Norges Bank Author-Name: Herman K. van Dijk Author-Workplace-Name: Erasmus University Rotterdam, VU University Amsterdam Title: Combination Schemes for Turning Point Predictions Abstract: This discussion paper resulted in a publication in 'The Quarterly Review of Economics and Finance', 2012, 52(4), 402-412.

We propose new forecast combination schemes for predicting turning points of business cycles. The combination schemes deal with the forecasting performance of a given set of models and possibly providing better turning point predictions. We consider turning point predictions generated by autoregressive (AR) and Markov-Switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach to both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and Euro area business cycles. Classification-JEL: C11, C15, C53, E37 Keywords: Turning Points, Markov-switching, Forecast Combination, Bayesian Model Averaging File-Url: https://papers.tinbergen.nl/11123.pdf File-Format: application/pdf File-Size: 221798 bytes Handle: RePEc:tin:wpaper:20110123