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Predicting recessions with a composite real-time dynamic probit model

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  • Proaño, Christian R.
  • Theobald, Thomas

Abstract

In this paper we propose a composite indicator for real-time recession forecasting based on alternative dynamic probit models. For this purpose, we use a large set of monthly macroeconomic and financial leading indicators from the German and US economies. Alternative dynamic probit regressions are specified through automated general-to-specific and specific-to-general lag selection procedures on the basis of slightly different initial sets. The resulting recession probability forecasts are then combined in order to decrease the volatility of the forecast errors and increase their forecasting accuracy. This procedure features not only good in-sample forecast statistics, but also good out-of-sample performances, as is illustrated using a real-time evaluation exercise.

Suggested Citation

  • Proaño, Christian R. & Theobald, Thomas, 2014. "Predicting recessions with a composite real-time dynamic probit model," International Journal of Forecasting, Elsevier, vol. 30(4), pages 898-917.
  • Handle: RePEc:eee:intfor:v:30:y:2014:i:4:p:898-917
    DOI: 10.1016/j.ijforecast.2014.02.007
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    Cited by:

    1. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, Research Program on Forecasting.
    2. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    3. Goodness C. Aye & Christina Christou & Luis A. Gil-Alana & Rangan Gupta, 2016. "Forecasting the Probability of Recessions in South Africa: The Role of Decomposed Term-Spread and Economic Policy Uncertainty," Working Papers 201680, University of Pretoria, Department of Economics.
    4. Schreiber, Sven & Soldatenkova, Natalia, 2016. "Anticipating business-cycle turning points in real time using density forecasts from a VAR," Journal of Macroeconomics, Elsevier, vol. 47(PB), pages 166-187.
    5. Christian Menden & Christian R. Proaño, 2017. "Dissecting the financial cycle with dynamic factor models," IMK Working Paper 183-2017, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    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.
    7. Menden, Christian & Proaño, Christian R., 2017. "Dissecting the financial cycle with dynamic factor models," BERG Working Paper Series 126, Bamberg University, Bamberg Economic Research Group.
    8. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions in Germany With Boosted Regression Trees," Macroeconomics and Finance Series 201505, University of Hamburg, Department of Socioeconomics.
    9. Schreiber, Sven, 2017. "Weather adjustment of economic output," Discussion Papers 2017/5, Free University Berlin, School of Business & Economics.

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