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Un análisis del ciclo económico de la República Dominicana bajo cambios de régimen
[Analysis of business cycle of the Dominican Republic using Markov Switching model]

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  • Cruz-Rodríguez, Alexis

Abstract

This paper presents a univariate model that analyzes systematic changes in the behavior of the business cycle in the Dominican Republic, capturing changes in average growth and identifying differences between contractions and expansions with respect to their persistence and duration. To do so, it uses the classic algorithm described by Hamilton (1990, 1991) that consists of two parts. In the first part, population parameters, including joint probability density of unobserved states, are estimated. In the second part, using a nonlinear filter and smoothed probabilities, probabilistic inferences are made about unobserved states. Our results suggest that the characteristics of the distribution functions estimated for each scheme differ, both in mean and standard deviation. Thus, for a recessive event or contraction, average quarterly growth was around -0.33% with a standard deviation of 0.45%, whereas for an expanding statistical event, estimates were 0.23% and 0.27%, respectively.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 54352.

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Date of creation: 30 Jun 2004
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Handle: RePEc:pra:mprapa:54352

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Keywords: Business cycle; regime switching models;

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References

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  1. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1.
  2. Sichel, D.E., 1988. "Business Cycle Asymmetry: A Deeper Look," Papers, Princeton, Department of Economics - Financial Research Center 85, Princeton, Department of Economics - Financial Research Center.
  3. Hans-Martin Krolzig & Michael Clements, 2000. "Business Cycle Asymmetries: Characterisation and Testing based on Markov-Switching Autoregressions," Economics Series Working Papers 2000-W32, University of Oxford, Department of Economics.
  4. Diebold & Rudebusch, . "Measuring Business Cycle: A Modern Perspective," Home Pages, University of Pennsylvania _061, University of Pennsylvania.
  5. James H. Stock & Mark W. Watson, 1993. "Introduction to "Business Cycles, Indicators and Forecasting"," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 1-10 National Bureau of Economic Research, Inc.
  6. 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.
  7. Hansen, Bruce E, 1992. "The Likelihood Ratio Test under Nonstandard Conditions: Testing the Markov Switching Model of GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 7(S), pages S61-82, Suppl. De.
  8. James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1.
  9. Christian A. Johnson, 2000. "Un Modelo de Switching para el Crecimiento en Chile," Working Papers Central Bank of Chile, Central Bank of Chile 84, Central Bank of Chile.
  10. McQueen, Grant & Thorley, Steven, 1993. "Asymmetric business cycle turning points," Journal of Monetary Economics, Elsevier, Elsevier, vol. 31(3), pages 341-362, June.
  11. Andrew J. Filardo & Stephen F. Gordon, 1993. "Business cycle durations," Research Working Paper, Federal Reserve Bank of Kansas City 93-11, Federal Reserve Bank of Kansas City.
  12. 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, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-96, November.
  13. 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.
  14. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 16(2), pages 127-52, April.
  15. Hamilton, James D, 1991. "A Quasi-Bayesian Approach to Estimating Parameters for Mixtures of Normal Distributions," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 9(1), pages 27-39, January.
  16. 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.
  17. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, Econometric Society, vol. 49(4), pages 1057-72, June.
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