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Probability and Severity of Recessions

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  • Rachidi Kotchoni
  • Dalibor Stevanovic

    ()

Abstract

This paper advances beyond the prediction of the probability of a recession by also considering its severity in terms of output loss and duration. First, Probit models are used to estimate the probability of a recession at period t + h from the information available at period t. Next, a Vector Autoregression (VAR) augmented with diffusion indices and an inverse Mills ratio (IMR) is fitted to selected measures of real economic activity. The latter model is used to generate two forecasts: an average forecast, and a forecast under the pessimistic assumption that a recession occurs at the forecast horizon. The severity of recessions is then predicted as the gap between these two forecasts. Finally, a zero-inated Poisson model is fitted to historical durations of recessions. Our empirical results suggest that U.S. recessions are fairly predictable, both in terms of occurrence and severity. Out-of-sample experiments suggest that the inclusion of the IMR in the VAR model significantly improves its forecasting performance.

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Bibliographic Info

Paper provided by CIRANO in its series CIRANO Working Papers with number 2013s-43.

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Date of creation: 01 Nov 2013
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Handle: RePEc:cir:cirwor:2013s-43

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Keywords: Duration of recessions; Forecasting Real Activity; Probability of Recessions; Probit; Vector Autoregression; Zero Inated Poisson.;

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  1. Issler, João Victor & Vahid, Farshid, 2002. "The Missing Link: Using the NBER Recession Indicator to Construct Coincident and Leading Indices of Economic Activity," Economics Working Papers (Ensaios Economicos da EPGE) 450, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
  2. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
  3. Marcelle Chauvet & Jeremy M. Piger, 2005. "A comparison of the real-time performance of business cycle dating methods," Working Papers 2005-021, Federal Reserve Bank of St. Louis.
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  6. Marcelle Chauvet & James D. Hamilton, 2005. "Dating Business Cycle Turning Points," NBER Working Papers 11422, National Bureau of Economic Research, Inc.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. Todd E. Clark & Kenneth D. West, 2004. "Using out-of-sample mean squared prediction errors to test the Martingale difference hypothesis," Research Working Paper RWP 04-03, Federal Reserve Bank of Kansas City.
  14. James D. Hamilton, 2010. "Calling Recessions in Real Time," NBER Working Papers 16162, National Bureau of Economic Research, Inc.
  15. Arturo Estrella & Frederic S. Mishkin, 1995. "Predicting U.S. Recessions: Financial Variables as Leading Indicators," NBER Working Papers 5379, 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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