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Modelling asymmetric persistence over the business cycle

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Author Info

  • Franses, Ph.H.B.F.
  • Paap, R.

Abstract

We address the issue of time varying persistence of shocks to macroeconomic time series variables by proposing a new and parsimonious time series model. Our model assumes that this time varying persistence depends on a linear combination of lagged explanatory variables, where this combination characterizes the business cycle regimes. The key feature of our model is that an autoregressive parameter takes larger values only when this indicator variable exceeds a stochastic threshold. The parameters and the (lags of the) variables that constitute the indicator variable have to be determined from the data. Other forms of censoring amount to straightforward extensions. Our application to US unemployment shows that the model fits very well. A linear combination of lagged (differenced) industrial production, oil price, interest spread and stock returns amounts to an adequate indicator of an upcoming recession, which corresponds with explosive behavior of unemployment. Also, the out-of-sample forecasts from our model oftentimes improve those from linear and other nonlinear models.

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File URL: http://repub.eur.nl/pub/1525/feweco19981203145733.pdf
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Bibliographic Info

Paper provided by Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute in its series Econometric Institute Research Papers with number EI 9852.

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Date of creation: 01 Jan 1998
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Handle: RePEc:ems:eureir:1525

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Related research

Keywords: asymmetric persistence; business cycle; censored regression; nonlinear time series;

References

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  1. Elwood, S. Kirk, 1998. "Is the persistence of shocks to output asymmetric?," Journal of Monetary Economics, Elsevier, Elsevier, vol. 41(2), pages 411-426, April.
  2. Estrella, Arturo & Mishkin, Frederic S., 1997. "The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank," European Economic Review, Elsevier, Elsevier, vol. 41(7), pages 1375-1401, July.
  3. Harvey, Campbell R., 1988. "The real term structure and consumption growth," Journal of Financial Economics, Elsevier, Elsevier, vol. 22(2), pages 305-333, December.
  4. Granger, Clive W. J. & Swanson, Norman R., 1997. "An introduction to stochastic unit-root processes," Journal of Econometrics, Elsevier, Elsevier, vol. 80(1), pages 35-62, September.
  5. Hamilton, James D, 1983. "Oil and the Macroeconomy since World War II," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 91(2), pages 228-48, April.
  6. Mork, Knut Anton, 1989. "Oil and Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 97(3), pages 740-44, June.
  7. Estrella, Arturo & Hardouvelis, Gikas A, 1991. " The Term Structure as a Predictor of Real Economic Activity," Journal of Finance, American Finance Association, American Finance Association, vol. 46(2), pages 555-76, June.
  8. Blanchard, Olivier J. & Summers, Lawrence H., 1987. "Hysteresis in unemployment," European Economic Review, Elsevier, Elsevier, vol. 31(1-2), pages 288-295.
  9. Olivier J. Blanchard & Lawrence H. Summers, 1986. "Hysteresis And The European Unemployment Problem," NBER Chapters, National Bureau of Economic Research, Inc, in: NBER Macroeconomics Annual 1986, Volume 1, pages 15-90 National Bureau of Economic Research, Inc.
  10. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, Elsevier, vol. 45(1-2), pages 39-70.
  11. Zoega, Gylfi, 1994. "Unemployment Persistence: Does the Size of the Shock Matter?," CEPR Discussion Papers, C.E.P.R. Discussion Papers 1082, C.E.P.R. Discussion Papers.
  12. Leybourne, S J & McCabe, B P M & Tremayne, A R, 1996. "Can Economic Time Series Be Differenced to Stationarity?," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 14(4), pages 435-46, October.
  13. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De.
  14. Grillenzoni, Carlo, 1993. "ARIMA Processes with ARIMA Parameters," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 11(2), pages 235-50, April.
  15. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 12(3), pages 299-308, July.
  16. Beaudry, Paul & Koop, Gary, 1993. "Do recessions permanently change output?," Journal of Monetary Economics, Elsevier, Elsevier, vol. 31(2), pages 149-163, April.
  17. Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 92(2), pages 307-28, April.
  18. Hamilton, James D., 1996. "Specification testing in Markov-switching time-series models," Journal of Econometrics, Elsevier, Elsevier, vol. 70(1), pages 127-157, January.
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Cited by:
  1. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2004. "Non-Linearities And Fractional Integration In The Us Unemployment Rate," Economics and Finance Discussion Papers, Economics and Finance Section, School of Social Sciences, Brunel University 04-17, Economics and Finance Section, School of Social Sciences, Brunel University.

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