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Anticipating business-cycle turning points in real time using density forecasts from a VAR

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  • Schreiber, Sven

Abstract

For 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).

Suggested Citation

  • Schreiber, Sven, 2014. "Anticipating business-cycle turning points in real time using density forecasts from a VAR," Discussion Papers 2014/2, Free University Berlin, School of Business & Economics.
  • Handle: RePEc:zbw:fubsbe:20142
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    Cited by:

    1. Jakob Fiedler & Josef Ruzicka & Thomas Theobald, 2019. "The Real-Time Information Content of Financial Stress and Bank Lending on European Business Cycles," IMK Working Paper 198-2019, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    2. Schreiber, Sven, 2017. "Weather adjustment of economic output," Discussion Papers 2017/5, Free University Berlin, School of Business & Economics.
    3. Proaño, Christian R. & Tarassow, Artur, 2018. "Evaluating the predicting power of ordered probit models for multiple business cycle phases in the U.S. and Japan," Journal of the Japanese and International Economies, Elsevier, vol. 50(C), pages 60-71.
    4. Pirschel, Inske, 2016. "Forecasting euro area recessions in real-time," Kiel Working Papers 2020, Kiel Institute for the World Economy (IfW).
    5. Pirschel, Inske, 2015. "Forecasting Euro Area Recessions in real-time with a mixed-frequency Bayesian VAR," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113031, Verein für Socialpolitik / German Economic Association.
    6. Erik Haustein & Sven Schreiber, 2016. "Adjusting production indices for varying weather effects," IMK Working Paper 171-2016, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.

    More about this item

    Keywords

    density forecasts; business-cycle turning points; real-time data; nowcasting; great recession;

    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

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