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Probability and Severity of Recessions

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  • Rachidi Kotchoni
  • Dalibor Stevanovic

Abstract

This paper tackles the prediction of the probability and severity of US recessions. We employ parsimonious Probit models to estimate the probability of a recession h periods ahead, for h varying between 1 and 8 quarters. A novel goodness-of-fit measure derived from the Kullback-Leibler Information Criterion is developed and used to select the regressors to include in the Probit models. Next, an autoregression (AR) augmented with inverse Mills ratio (IMR) and diffusion indices (DI) is fitted to selected measures of real economic activity. The resulting “IMR-DI-AR” model is used to generate forecasts conditional on optimistic and pessimistic scenarios for the horizon of interest. The severity of recessions is defined as the gap between the pessimistic scenario and the recent trend of the series. For a time series of GDP growth, our measure of recession severity has the interpretation of the output loss. Our results support that U.S. recessions are predictable to a great extent, both in terms of occurrence and severity. All recessions are not alike: some are more predictable than others while some are more severe than expected.

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Bibliographic Info

Paper provided by CIRPEE in its series Cahiers de recherche with number 1341.

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Date of creation: 2013
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Handle: RePEc:lvl:lacicr:1341

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Keywords: Forecasting; Principal Components; Probit; Real Activity; Recessions;

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  1. Serena Ng & Jonathan H. Wright, 2013. "Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1120-54, December.
  2. Alquist, Ron & Kilian, Lutz, 2007. "What Do We Learn from the Price of Crude Oil Futures?," CEPR Discussion Papers 6548, C.E.P.R. Discussion Papers.
  3. Chauvet, Marcelle & Piger, Jeremy, 2008. "A Comparison of the Real-Time Performance of Business Cycle Dating Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 42-49, January.
  4. Marcelle Chauvet & James D. Hamilton, 2005. "Dating Business Cycle Turning Points," NBER Working Papers 11422, National Bureau of Economic Research, Inc.
  5. Michael Dueker & Katrin Assenmacher-Wesche, 2010. "Forecasting macro variables with a Qual VAR business cycle turning point index," Applied Economics, Taylor & Francis Journals, vol. 42(23), pages 2909-2920.
  6. Issler, Joao Victor & Vahid, Farshid, 2006. "The missing link: using the NBER recession indicator to construct coincident and leading indices of economic activity," Journal of Econometrics, Elsevier, vol. 132(1), pages 281-303, May.
  7. Watson, Mark W, 1994. "Business-Cycle Durations and Postwar Stabilization of the U.S. Economy," American Economic Review, American Economic Association, vol. 84(1), pages 24-46, March.
  8. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
  9. Anderson, Heather M. & Vahid, Farshid, 2001. "Predicting The Probability Of A Recession With Nonlinear Autoregressive Leading-Indicator Models," Macroeconomic Dynamics, Cambridge University Press, vol. 5(04), pages 482-505, September.
  10. Michael Dueker, 2005. "Dynamic Forecasts of Qualitative Variables: A Qual VAR Model of U.S. Recessions," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 96-104, January.
  11. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-96, November.
  12. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
  13. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
  14. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  15. James D. Hamilton, 2010. "Calling Recessions in Real Time," NBER Working Papers 16162, National Bureau of Economic Research, Inc.
  16. Francis X. Diebold, 2012. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," PIER Working Paper Archive 12-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  17. Ray C. Fair, 1993. "Estimating Event Probabilities from Macroeconometric Models Using Stochastic Simulation," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 157-178 National Bureau of Economic Research, Inc.
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