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This Is What The Leading Indicators Lead

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  • Maximo Camacho

    (Universitat Autonoma de Barcelona)

  • Gabriel Perez-Quiros

    (European Central Bank)

Abstract

The purpose of this paper is two-fold. First, we compare the accuracy of previous studies that analyze the ability of the Composite Index of Leading Indicators (CLI) for predicting turning points. Alternative filters are also proposed. For these comparisons, we adapt the tests developed by Diebold and Mariano (1995) to the business cycles framework. Second, we combine different approaches to produce a filter that transforms the monthly CLI growth figures into a more intuitive measure of the probability of recession. We examine the predictive power of the CLI for movements in GDP.For the first objective, we analyze the accuracy of the following models: First, we generalize the analysis of Hamilton and Perez-Quiros (1996) describing how linear univariate and bivariate models can be used to forecast nonlinear phenomena such as turning points. We update their study of multivariate Markov switching models. Second, we extend the Smooth Transition Regression methodology to a VAR context. We identify the transition function as the filter that shows the probability of locating the economy between the different states. Third, we analyze an expansion of the probit model suggested in Estrella and Mishkin (1998). Finally, we propose a new methodology based upon adaptive kernel estimation for predicting recessions nonparametrically. Despite the good in-sample performance of the switching regimes model, we conclude that a simple linear univariate model for GDP is more accurate than any bivariate specification in real-time.For the second objective, we suggest that a combination of the forecasts may exploit more leading information from the CLI than any of the individual forecasting models. Combining forecasts of growth, we apply the rule proposed by Granger and Ramanathan (1984). Combining forecasts of recessions, we use a method in the spirit of Li and Dorfman (1996). We prove that a combination of the switching regimes (the best within recessions) and the nonparametic (the best within expansions) is as good as a combination of all the models. The out-of-sample results indicate that the real-time combination presents the most accurate statistical forecast of both GDP growth and recessions. Thus, we conclude that the CLI is useful in anticipating both turning points and output growth. In addition, in contrast to Hess and Iwata (1997), we find that nonlinear specifications perform better than simpler linear models at reproducing the business cycles features of real GDP.An illustration of the operation of this filter shows that the same CLI growth rate contains very different information about the probability of an imminent recession depending on the period considered.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 132.

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Date of creation: 05 Jul 2000
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Handle: RePEc:sce:scecf0:132

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Postal: CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain
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References

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  1. Arturo Estrella & Frederic S. Mishkin, 1996. "Predicting U.S. recessions: financial variables as leading indicators," Research Paper 9609, Federal Reserve Bank of New York.
  2. Hamilton, James D & Perez-Quiros, Gabriel, 1996. "What Do the Leading Indicators Lead?," The Journal of Business, University of Chicago Press, vol. 69(1), pages 27-49, January.
  3. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  4. Birchenhall, Chris R, et al, 1999. "Predicting U.S. Business-Cycle Regimes," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 313-23, July.
  5. Clive W. Granger & Timo Terasvirta & Heather M. Anderson, 1993. "Modeling Nonlinearity over the Business Cycle," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 311-326 National Bureau of Economic Research, Inc.
  6. Diebold, Francis X & Rudebusch, Glenn D, 1989. "Scoring the Leading Indicators," The Journal of Business, University of Chicago Press, vol. 62(3), pages 369-91, July.
  7. Hess, Gregory D & Iwata, Shigeru, 1997. "Measuring and Comparing Business-Cycle Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 432-44, October.
  8. Li, David T & Dorfman, Jeffrey H, 1996. "Predicting Turning Points through the Integration of Multiple Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 421-28, October.
  9. Chris R. Birchenhall & Marianne Sensier & Denise R. Osborn, 2000. "Predicting Uk Business Cycle Regimes," Computing in Economics and Finance 2000 134, Society for Computational Economics.
  10. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
  11. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  12. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
  13. James H. Stock & Mark W. Watson, 1993. "A Procedure for Predicting Recessions with Leading Indicators: Econometric Issues and Recent Experience," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 95-156 National Bureau of Economic Research, Inc.
  14. Andrew J. Filardo, 1999. "How reliable are recession prediction models?," Economic Review, Federal Reserve Bank of Kansas City, issue Q II, pages 35-55.
  15. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
  16. Denis Kwiatkowski & Peter C.B. Phillips & Peter Schmidt, 1991. "Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We That Economic Time Series Have a Unit Root?," Cowles Foundation Discussion Papers 979, Cowles Foundation for Research in Economics, Yale University.
  17. Wecker, William E, 1979. "Predicting the Turning Points of a Time Series," The Journal of Business, University of Chicago Press, vol. 52(1), pages 35-50, January.
  18. Lobato, Ignacio N & Robinson, Peter M, 1998. "A Nonparametric Test for I(0)," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 475-95, July.
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