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Estimating and Testing Multiple Structural Changes in Models with Endogenous Regressors

  • Pierre Perron

    ()

    (Department of Economics, Boston University)

  • Yohei Yamamoto

    ()

    (Department of Economics, Boston University)

We consider the problem of estimating and testing for multiple breaks in a single equation framework with regressors that are endogenous, i.e., correlated with the errors. First, we show based on standard assumptions about the regressors, instruments and errors that the second stage regression of the instrumental variable (IV) procedure involves regressors and errors that satisfy all the assumptions in Perron and Qu (2006) so that the results about consistency, rate of convergence and limit distributions of the estimates of the break dates, as well as the limit distributions of the tests, are obtained as simple consequences. More importantly from a practical perspective, we show that even in the presence of endogenous regressors, it is still preferable to simply estimate the break dates and test for structural change using the usual ordinary least-squares (OLS) framework. It delivers estimates of the break dates with higher precision and tests with higher power compared to those obtained using an IV method. To illustrate the relevance of our theoretical results, we consider the stability of the New Keynesian hybrid Phillips curve. IV-based methods do not indicate any instability. On the other hand, OLS-based ones strongly indicate a change in 1991:1 and that after this date the model looses all explanatory power.

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Paper provided by Boston University - Department of Economics in its series Boston University - Department of Economics - Working Papers Series with number wp2008-017.

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Length: 33
Date of creation: Oct 2008
Date of revision:
Handle: RePEc:bos:wpaper:wp2008-017
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  1. Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  2. Perron, Pierre & Qu, Zhongjun, 2006. "Estimating restricted structural change models," Journal of Econometrics, Elsevier, vol. 134(2), pages 373-399, October.
  3. Kejriwal, Mohitosh & Perron, Pierre, 2008. "The limit distribution of the estimates in cointegrated regression models with multiple structural changes," Journal of Econometrics, Elsevier, vol. 146(1), pages 59-73, September.
  4. Tatevik Sekhposyan & Barbara Rossi, 2008. "Has modelsí forecasting performance for US output growth and inflation changed over time, and when?," Working Papers 09-02, Duke University, Department of Economics.
  5. James H. Stock & Mark W. Watson, 2006. "Why Has U.S. Inflation Become Harder to Forecast?," NBER Working Papers 12324, National Bureau of Economic Research, Inc.
  6. 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.
  7. Kejriwal, Mohitosh & Perron, Pierre, 2010. "Testing for Multiple Structural Changes in Cointegrated Regression Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(4), pages 503-522.
  8. 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.
  9. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-56, July.
  10. Kurmann, Andre, 2007. "VAR-based estimation of Euler equations with an application to New Keynesian pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(3), pages 767-796, March.
  11. Hall, Alastair R. & Han, Sanggohn & Boldea, Otilia, 2008. "Asymptotic Distribution Theory for Break Point Estimators in Models Estimated via 2SLS," MPRA Paper 9472, University Library of Munich, Germany.
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