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Application of Three Alternative Approaches to Identify Business Cycles in Peru

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  • Rodriguez Gabriel

    ()
    (Universidad of Ottawa and Central Bank of Peru)

Abstract

Three alternative econometric approaches are used to estimate business cycles in the Peruvian economy. These approaches are the Plucking model due to Friedman (1964, 1993), the Markov Switching model proposed by Hamilton (1989) and the Smooth Transition Autoregressive (STAR) model suggested by Teräsvirta (1994). The results show strong rejection of the null hypothesis of linearity, presence of asymmetries and nonlinearities. Furthermore, the methods allow to find the principal episodes of recession for the Peruvian economy.

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Bibliographic Info

Paper provided by Banco Central de Reserva del Perú in its series Working Papers with number 2007-007.

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Date of creation: May 2007
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Handle: RePEc:rbp:wpaper:2007-007

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Keywords: Asymmetries; Business Regional Fluctuations; Markov Switching; Transitory and Permanent Components;

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  1. Kim, C-J & Nelson, C-R, 1997. "Friedman's Plucking Model of Business Fluctuations : Tests and Estimates of Permanent and Transitory Components," Discussion Papers in Economics at the University of Washington 97-06, Department of Economics at the University of Washington.
  2. Taylor, Mark P & Peel, David A & Sarno, Lucio, 2001. "Nonlinear Mean-Reversion in Real Exchange Rates: Toward a Solution to the Purchasing Power Parity Puzzles," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(4), pages 1015-42, November.
  3. Lundbergh, Stefan & Teräsvirta, Timo, 1998. "Modelling economic high-frequency time series with STAR-STGARCH models," Working Paper Series in Economics and Finance 291, Stockholm School of Economics.
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  7. Friedman, Milton, 1993. "The "Plucking Model" of Business Fluctuations Revisited," Economic Inquiry, Western Economic Association International, vol. 31(2), pages 171-77, April.
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  15. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
  16. Francis X. Diebold & Glenn Rudebusch & Daniel Sichel, 1993. "Further Evidence on Business-Cycle Duration Dependence," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 255-284 National Bureau of Economic Research, Inc.
  17. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
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  19. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De.
  20. Clark, Peter K, 1987. "The Cyclical Component of U.S. Economic Activity," The Quarterly Journal of Economics, MIT Press, vol. 102(4), pages 797-814, November.
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  22. Falk, Barry, 1986. "Further Evidence on the Asymmetric Behavior of Economic Time Series over the Business Cycle," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1096-1109, October.
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  24. Sichel, Daniel E, 1994. "Inventories and the Three Phases of the Business Cycle," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 269-77, July.
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  27. 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.
  28. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
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