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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). --

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Paper provided by Free University Berlin, School of Business & Economics in its series Discussion Papers with number 2014/2.

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Date of creation: 2014
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Handle: RePEc:zbw:fubsbe:20142

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

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  1. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
  2. Banbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2007. "Bayesian VARs with Large Panels," CEPR Discussion Papers 6326, C.E.P.R. Discussion Papers.
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  8. Ng, Eric C.Y., 2012. "Forecasting US recessions with various risk factors and dynamic probit models," Journal of Macroeconomics, Elsevier, vol. 34(1), pages 112-125.
  9. Österholm, Pär, 2012. "The limited usefulness of macroeconomic Bayesian VARs when estimating the probability of a US recession," Journal of Macroeconomics, Elsevier, vol. 34(1), pages 76-86.
  10. Thomas Theobald, 2012. "Real-time Markov Switching and Leading Indicators in Times of the Financial Crisis," IMK Working Paper 98-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
  11. James D. Hamilton, 2010. "Calling Recessions in Real Time," NBER Working Papers 16162, National Bureau of Economic Research, Inc.
  12. John W. Galbraith & Simon van Norden, 2012. "Assessing gross domestic product and inflation probability forecasts derived from Bank of England fan charts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(3), pages 713-727, 07.
  13. Henri Nyberg, 2010. "Dynamic probit models and financial variables in recession forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 215-230.
  14. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
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