In this issue we have Combining Forecast Densities from VARs with Uncertain Instabilities ; Dating and forecasting turning points by Bayesian clustering with dynamic structure; Consumer confidence indices and short-term forecasting of consumption ; Money Demand Stability and Inflation: Prediction in the Four Largest EMU Countries.
Jore, A. S., Vahey, S. P.
|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.|
|By:||Sylvia Kaufmann (Oesterreichische Nationalbank, Economic Studies Division, P.O. Box 61, A-1010 Vienna,)|
|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.|
E. Philip Davis
|Recently there has been growing interest in examining the potential short-term link between survey-based confidence indicators and real economic activity, notably for macroeconomic policy making. This paper builds on previous studies to establish whether there is a short-term predictive relationship between measures of consumer confidence and actual consumption, which could be used for forecasting, in a range of major industrial countries. It then extends such previous analyses by assessing whether this relation has changed over time, and whether we can attribute any time-varying relation to structural developments in the economy, such as financial deepening and the increasing role of house prices in determination of consumption.|
|By:||Muriel Nguiffo-Boyom Author-Email1: email@example.com|
|This paper presents a new indicator of economic activity for Luxembourg, developed using a large database of 99 economic and financial time series. The methodology used corresponds to the generalised dynamic-factor models that has been introduced in the literature by Forni et alii (2005), and the model as been estimated over the period from June 1995 to June 2007. Several means have been used to evaluate its forecasting performances and results are satisfactory. They in particular give clear evidence that our indicator allows to obtain better forecasts of the GDP growth relative to a more classical approach that relies on GDP past values only. This indicator is calculated on an experimental basis and changes may be integrated.|
|By:||Abelardo Salazar Neaves
|In this paper we analyze the money demand functions of the four largest EMU countries and of the four-country (EMU-4) aggregate. We identify reasonable and stable money demand relationships for Germany, France and Spain as well as the EMU-4 aggregate. For the case of Italy, results are less clear. From the estimated money demand functions, we derive both EMU-4 and country-specific measures of money overhang. We find that the EMU-4 overhang measure strongly correlates with the countryspecific measures, particularly since the start of EMU, and is useful to predict country-specific inflation. However, it generally does not encompass country-specific money overhang measures as predictors of inflation. Hence, aggregate money overhang is an important, but by far not an exhaustive, indicator fort he disaggregate level|
|Keywords:||Money demand, stability, money overhang, inflation forecast|
Taken from the NEP-FOR mailing list edited by Rob Hyndman.