nep-ets 2008-08-31 papers

on Econometric Time Series

Issue of 2008‒08‒31
two papers chosen by
Yong Yin
SUNY at Buffalo

Combining Forecast Densities from VARs with Uncertain Instabilities By James Mitchell; Jore, A. S., Vahey, S. P.
Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data. By Sylvia Kaufmann

Combining Forecast Densities from VARs with Uncertain Instabilities

By:	James Mitchell; Jore, A. S., Vahey, S. P.
Abstract:	Clark and McCracken (2008) argue that combining real-time point forecasts from VARs of output, prices and interest rates improves point forecast accuracy in the presence of uncertain model instabilities. In this paper, we generalize their approach to consider forecast density combinations and evaluations. Whereas Clark and Mc-Cracken (2008) show that the point forecast errors from particular equal-weight pair wise averages are typically comparable or better than benchmark univariate time series models, we show that neither approach produces accurate real-time forecast densities for recent US data. If greater weight is given to models that allow for the shifts in volatilities associated with the Great Moderation, predictive density accuracy improves substantially.
Date:	2008–01
URL:	http://d.repec.org/n?u=RePEc:nsr:niesrd:303&r=ets

Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data.

By:	Sylvia Kaufmann (Oesterreichische Nationalbank, Economic Studies Division, P.O. Box 61, A-1010 Vienna,)
Abstract:	The information contained in a large panel data set is used to date historical turning points of the Austrian business cycle and to forecast future ones. We estimate groups of series with similar time series dynamics and link the groups with a dynamic structure. The dynamic structure identifies a group of leading and a group of coincident series. Robust results across data vintages are obtained when series specific information is incorporated in the design of the prior group probability distribution. The results are consistent with common expectations, in particular the group of leading series includes Austrian confidence indicators and survey data, German survey indicators, some trade data, and, interestingly, the Austrian and the German stock market indices. The forecast evaluation confirms that the Markov switching panel with dynamic structure performs well when compared to other specifications.
Keywords:	Bayesian clustering, parameter heterogeneity, latent dynamic structure, Markov switching, panel data, turning points.
JEL:	C23 E32
Date:	2008–06–19
URL:	http://d.repec.org/n?u=RePEc:onb:oenbwp:144&r=ets

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