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Testing for Parameter Stability in Dynamic Models Across Frequencies

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Author Info

  • Bertrand Candelon

    ()
    (University of Maastricht - Department of Economics)

  • Gianluca Cubadda

    ()
    (University of Rome II - Department of Financial and Quantitative Economics)

Abstract

This paper contributes to the econometric literature on structural breaks by proposing a test for parameter stability in VAR models at a particular frequency w, where w [0, p]. When a dynamic model is affected by a structural break, the new tests allow for detecting which frequencies of the data are responsible for parameter instability. If the model is locally stable at the frequencies of interest, the whole sample size can be then exploited despite the presence of a break. Two empirical examples illustrate that local stability can concern only the lower frequencies (change in the U.S. monetary policy in the early 80'(s) or higher frequencies (decrease in the postwar U.S. productivity).

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Bibliographic Info

Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 82.

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Length: 22
Date of creation: 31 May 2006
Date of revision:
Handle: RePEc:rtv:ceisrp:82

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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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Web page: http://www.ceistorvergata.it
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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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Web: http://www.ceistorvergata.it

Related research

Keywords: Structural breaks; spectral analysis; productivity slowdown; yield curve;

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References

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  1. Cubadda, Gianluca, 2001. " Complex Reduced Rank Models for Seasonally Cointegrated Time Series," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 63(4), pages 497-511, September.
  2. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-40, September.
  3. Donald W.K. Andrews, 1990. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Cowles Foundation Discussion Papers 943, Cowles Foundation for Research in Economics, Yale University.
  4. Lawrence J. Christiano & Robert J. Vigfusson, 2001. "Maximum likelihood in the frequency domain: the importance of time-to-plan," Working Paper 0106, Federal Reserve Bank of Cleveland.
  5. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, vol. 132(2), pages 363-378, June.
  6. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
  7. Zhongjun Qu & Pierre Perron, 2005. "Estimating and testing structural changes in multivariate regressions," Boston University - Department of Economics - Working Papers Series WP2005-012, Boston University - Department of Economics.
  8. Peter Reinhard Hansen, 2000. "Structural Changes in the Cointegrated Vector Autoregressive Model," Working Papers 2000-20, Brown University, Department of Economics.
  9. Arturo Estrella & Anthony P. Rodrigues & Sebastian Schich, 2000. "How stable is the predictive power of the yield curve? evidence from Germany and the United States," Staff Reports 113, Federal Reserve Bank of New York.
  10. Michael Dotsey, 1998. "The predictive content of the interest rate term spread for future economic growth," Economic Quarterly, Federal Reserve Bank of Richmond, issue Sum, pages 31-51.
  11. Candelon, Bertrand & Lutkepohl, Helmut, 2001. "On the reliability of Chow-type tests for parameter constancy in multivariate dynamic models," Economics Letters, Elsevier, vol. 73(2), pages 155-160, November.
  12. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : II. New directions," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 309-341.
  13. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
  14. Arturo Estrella & Gikas A. Hardouvelis, 1989. "The term structure as a predictor of real economic activity," Research Paper 8907, Federal Reserve Bank of New York.
  15. Bruce E. Hansen, 2001. "The New Econometrics of Structural Change: Dating Breaks in U.S. Labour Productivity," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 117-128, Fall.
  16. Jushan Bai, 1999. "Vector Autoregressive Models with Structural Changes in Regression Coefficients and in Variance-Covariance Matrices," CEMA Working Papers 24, China Economics and Management Academy, Central University of Finance and Economics, revised Oct 2000.
  17. Centoni, Marco & Cubadda, Gianluca, 2003. "Measuring the business cycle effects of permanent and transitory shocks in cointegrated time series," Economics Letters, Elsevier, vol. 80(1), pages 45-51, July.
  18. Plosser, Charles I. & Geert Rouwenhorst, K., 1994. "International term structures and real economic growth," Journal of Monetary Economics, Elsevier, vol. 33(1), pages 133-155, February.
  19. Bai, Jushan & Lumsdaine, Robin L & Stock, James H, 1998. "Testing for and Dating Common Breaks in Multivariate Time Series," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 395-432, July.
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Cited by:
  1. Vincenzo Atella & Marco Centoni & Gianluca Cubadda, 2007. "Technology shocks, structural breaks and the effects on the business cycle," CEIS Research Paper 105, Tor Vergata University, CEIS.

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