Suchergebnisse

This paper compares two single-equation approaches from the recent nowcast literature: Mixed-data sampling (MIDAS) regressions and bridge equations. Both approach are used to nowcast a low-frequency variable such as quarterly GDP growth by higher-frequency business cycle indicators. Three differences between the approaches are discussed: 1) MIDAS is a direct multi-step nowcasting tool, whereas bridge equations are based on iterated forecasts; 2) MIDAS equations employ empirical weighting of high-frequency predictor observations with functional lag polynomials, whereas the weights of indicator observations in bridge equations are partly fixed stemming from time aggregation. 3) MIDAS equations can consider current-quarter leads of high-frequency indicators in the regression, whereas bridge equations typically do not. However, the conditioning set for nowcasting includes the most recent indicator observations in both approaches. To discuss the differences between the approaches in isolation, intermediate specifications between MIDAS and bridge equations are provided. The alternative models are compared in an empirical application to nowcasting GDP growth in the Euro area given a large set of business cycle indicators.

This paper considers factor forecasting with national versus factor forecasting with international data. We forecast German GDP based on a large set of about 500 time series, consisting of German data as well as data from Euro-area and G7 countries. For factor estimation, we consider standard principal components as well as variable preselection prior to factor estimation using targeted predictors following Bai and Ng [Forecasting economic time series using targeted predictors, Journal of Econometrics 146 (2008), 304-317]. The results are as follows: Forecasting without data preselection favours the use of German data only, and no additional information content can be extracted from international data. However, when using targeted predictors for variable selection, international data generally improves the forecastability of German GDP. -- forecasting ; factor models ; international data ; variable selection