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On detecting end-of-sample instabilities

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  • Fabio Busetti

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Abstract

Tests of parameter instabilities are likely to have low power when change-points occur towards the end of the sample. This paper considers various modifications of existing tests and introduces new statistics designed to have high power in such circumstances. The properties of both Wald-type tests of a one-time shift in the parameters and locally most powerful (LMP) tests against the hypothesis of random walk coefficients are examined. It is proposed to take functionals of the Wald and LMP statistics such that either the set of possible change-points is restricted to the last part of the sample or the occurrence of change-points is given increasing weight throughout the sample. For the case of an unknown end-of-sample change-point, the LMP-type tests appear to have, in general, better properties than Wald-type tests, even against the hypothesis of a one-time shift in the parameters. Empirical illustrations describe the use of the tests for detecting structural changes at the time of the 'Great Recession'.

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Bibliographic Info

Paper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 881.

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Date of creation: Sep 2012
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Handle: RePEc:bdi:wptemi:td_881_12

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Related research

Keywords: structural breaks; time-varying parameters;

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  1. Nyblom, Jukka & Harvey, Andrew, 1999. "Tests of Common Stochastic Trends," Cambridge Working Papers in Economics 9902, Faculty of Economics, University of Cambridge.
  2. Graham Elliott & Ulrich K. M´┐Żller, 2006. "Efficient Tests for General Persistent Time Variation in Regression Coefficients -super-1," Review of Economic Studies, Oxford University Press, vol. 73(4), pages 907-940.
  3. Fabio Busetti & Andrew Harvey, 2010. "Tests of strict stationarity based on quantile indicators," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(6), pages 435-450, November.
  4. Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Universite de Montreal, Departement de sciences economiques.
  5. Inoue, Atsushi, 2001. "Testing For Distributional Change In Time Series," Econometric Theory, Cambridge University Press, vol. 17(01), pages 156-187, February.
  6. Ploberger, Werner & Kramer, Walter & Kontrus, Karl, 1989. "A new test for structural stability in the linear regression model," Journal of Econometrics, Elsevier, vol. 40(2), pages 307-318, February.
  7. Fabio Busetti & Andrew Harvey, 2011. "When is a Copula Constant? A Test for Changing Relationships," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(1), pages 106-131, Winter.
  8. 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.
  9. Donald W.K. Andrews & Werner Ploberger, 1992. "Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative," Cowles Foundation Discussion Papers 1015, Cowles Foundation for Research in Economics, Yale University.
  10. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
  11. Donald W.K. Andrews, 2002. "End-of-Sample Instability Tests," Cowles Foundation Discussion Papers 1369, Cowles Foundation for Research in Economics, Yale University.
  12. Andrews, Donald W. K. & Lee, Inpyo & Ploberger, Werner, 1996. "Optimal changepoint tests for normal linear regression," Journal of Econometrics, Elsevier, vol. 70(1), pages 9-38, January.
  13. Andrews, Donald W.K. & Kim, Jae-Young, 2006. "Tests for Cointegration Breakdown Over a Short Time Period," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 379-394, October.
  14. Busetti, Fabio & Taylor, A. M. Robert, 2004. "Tests of stationarity against a change in persistence," Journal of Econometrics, Elsevier, vol. 123(1), pages 33-66, November.
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