Anticipating business-cycle turning points in real time using density forecasts from a VAR
AbstractFor the timely detection of business-cycle turning points we suggest to use mediumsized linear systems (subset VARs with automated zero restrictions) to forecast the relevant underlying variables, and to derive the probability of the turning point from the forecast density as the probability mass below (or above) a given threshold value. We show how this approach can be used in real time in the presence of data publication lags and how it can capture the part of the data revision process that is systematic. Then we apply the method to US and German monthly data. In an out-of-sample exercise (for 2007-2012/13) the turning points can be signalled before the official data publication confirms them (but not before they happened in reality). --
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Bibliographic InfoPaper provided by Free University Berlin, School of Business & Economics in its series Discussion Papers with number 2014/2.
Date of creation: 2014
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density forecasts; business-cycle turning points; real-time data; nowcasting; great recession;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-02-02 (All new papers)
- NEP-ETS-2014-02-02 (Econometric Time Series)
- NEP-FOR-2014-02-02 (Forecasting)
- NEP-MAC-2014-02-02 (Macroeconomics)
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