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Testing for Factor Loading Structural Change under Common Breaks

  • YAMAMOTO, Yohei
  • TANAKA, Shinya

This paper proposes a new test for factor loading structural change in dynamic factor models. The proposed test is robust to the nonmonotonic power problem that occurs if the factor loadings exhibit structural changes at common dates over cross-sections. To illustrate the usefulness of our test, we first show that the leading test proposed by Breitung and Eickmeier (2011) exhibits nonmonotonic power, essentially because the breaks are considered as spurious factors with stable factor loadings. We use both local and non-local asymptotic frameworks to investigate the power of their test. The new test eliminates the effects of the spurious factors by maximizing the test statistic over possible numbers of the original factors. This approach is effective because the original factors are not identified under the alternative hypothesis. Monte Carlo simulations and an empirical example using U.S. Treasury yield curve data clearly illustrate the validity of the asymptotic power analysis and usefulness of the proposed test.

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File URL: http://hermes-ir.lib.hit-u.ac.jp/rs/bitstream/10086/26010/1/070econDP13-17.pdf
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Paper provided by Graduate School of Economics, Hitotsubashi University in its series Discussion Papers with number 2013-17.

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Length: [1], 48 p.
Date of creation: Dec 2013
Date of revision:
Handle: RePEc:hit:econdp:2013-17
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Web page: http://www.econ.hit-u.ac.jp/

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  1. Breitung, Jörg & Eickmeier, Sandra, 2011. "Testing for structural breaks in dynamic factor models," Journal of Econometrics, Elsevier, vol. 163(1), pages 71-84, July.
  2. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
  3. Pierre Perron & Yohei Yamamoto, 2012. "On the Usefulness or Lack Thereof of Optimality Criteria for Structural Change Tests," Global COE Hi-Stat Discussion Paper Series gd12-258, Institute of Economic Research, Hitotsubashi University.
  4. Dukpa Kim & Pierre Perron, 2006. "Assessing the Relative Power of Structural Break Tests Using a Framework Based on the Approximate Bahadur Slope," Boston University - Department of Economics - Working Papers Series WP2006-063, Boston University - Department of Economics.
  5. Ai Deng & Pierre Perron, 2005. "A Non-local Perspective on the Power Properties of the CUSUM and CUSUM of Squares Tests for Structural Change," Boston University - Department of Economics - Working Papers Series WP2005-047, Boston University - Department of Economics.
  6. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  7. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
  8. Chen, Liang & Dolado, Juan J. & Gonzalo, Jesús, 2014. "Detecting big structural breaks in large factor models," Journal of Econometrics, Elsevier, vol. 180(1), pages 30-48.
  9. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
  10. Nelson, Forrest D & Savin, N E, 1990. "The Danger of Extrapolating Asymptotic Local Power," Econometrica, Econometric Society, vol. 58(4), pages 977-81, July.
  11. Alastair R. Hall, 2000. "Covariance Matrix Estimation and the Power of the Overidentifying Restrictions Test," Econometrica, Econometric Society, vol. 68(6), pages 1517-1528, November.
  12. 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.
  13. 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.
  14. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
  15. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, 05.
  16. 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.
  17. Perron, P., 1991. "A Test for Changes in a Polynomial Trend Functions for a Dynamioc Time Series," Papers 363, Princeton, Department of Economics - Econometric Research Program.
  18. Bai, Jushan, 2010. "Common breaks in means and variances for panel data," Journal of Econometrics, Elsevier, vol. 157(1), pages 78-92, July.
  19. Perron, Pierre, 1990. "Testing for a Unit Root in a Time Series with a Changing Mean," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 153-62, April.
  20. Xu Han & Atsushi Inoue, 2011. "Tests for Parameter Instability in Dynamic Factor Models," TERG Discussion Papers 306, Graduate School of Economics and Management, Tohoku University, revised May 2013.
  21. Amengual, Dante & Watson, Mark W., 2007. "Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 91-96, January.
  22. Kejriwal, Mohitosh, 2009. "Tests for a mean shift with good size and monotonic power," Economics Letters, Elsevier, vol. 102(2), pages 78-82, February.
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