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Tests to Disentangle Breaks in Intercept from Slope in Linear Regression Models with Application to Management Performance in the Mutual Fund Industry

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  • Jose Olmo
  • William Pouliot

Abstract

This article introduces a U-statistic type process that is fashioned from a kernal which can depend on nuisance parameters. It is shown that this process can accommodate, in a straightforward manner, anti-symmetric kernels, which have proved useful for detecting changing patterns in the dynamics of time series, and weight functions. Weight functions have been shown to improve the power of test statistics employed to detect these changing patterns throughout the evaluation perios; early and late as well. Theory and related test statistics are developed here and applied to detection of structural breaks in linear regression models (LRM). This flexibility is exploited to develop tests to detect changes in intercept or slope in LRMs that are robust to changes in the rest of medal parameters. The statistics developed here are applied to detect changing patterns in mutual fund manager's stock selecting ability over the period 2001 to 2010.

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File URL: ftp://ftp.bham.ac.uk/pub/RePEc/pdf/14-02.pdf
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Bibliographic Info

Paper provided by Department of Economics, University of Birmingham in its series Discussion Papers with number 14-02.

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Length: 28 pages
Date of creation: Mar 2014
Date of revision:
Handle: RePEc:bir:birmec:14-02

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Postal: Edgbaston, Birmingham, B15 2TT
Web page: http://www.economics.bham.ac.uk
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Related research

Keywords: Change-Point tests; CUSUM test; Linear regression models; Stochastic processes; U-Statistics;

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References

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  1. Burcu Kapar & William Pouliot, 2013. "Multiple Change-Point Detection in Linear Regression Models via U-Statistic Type Processes," Discussion Papers 13-13, Department of Economics, University of Birmingham.
  2. Barras, Laurent & Scaillet, Olivier & Wermers, Russ, 2009. "False discoveries in mutual fund performance: Measuring luck in estimated alphas," CFR Working Papers 06-02, University of Cologne, Centre for Financial Research (CFR).
  3. Perron, P, 1988. "The Great Crash, The Oil Price Shock And The Unit Root Hypothesis," Papers 338, Princeton, Department of Economics - Econometric Research Program.
  4. 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.
  5. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
  6. 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.
  7. Olmo, Jose & Pilbeam, Keith & Pouliot, William, 2011. "Detecting the presence of insider trading via structural break tests," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2820-2828, November.
  8. Csörgo, Miklós & Horváth, Lajos, 1988. "Invariance principles for changepoint problems," Journal of Multivariate Analysis, Elsevier, vol. 27(1), pages 151-168, October.
  9. 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.
  10. Donald W. K. Andrews, 2003. "Tests for Parameter Instability and Structural Change with Unknown Change Point: A Corrigendum," Econometrica, Econometric Society, vol. 71(1), pages 395-397, January.
  11. Hansen, Bruce E., 1992. "Testing for parameter instability in linear models," Journal of Policy Modeling, Elsevier, vol. 14(4), pages 517-533, August.
  12. Hansen, B.E., 1991. "Inference when a Nuisance Parameter is Not Identified Under the Null Hypothesis," RCER Working Papers 296, University of Rochester - Center for Economic Research (RCER).
  13. Altissimo, Filippo & Corradi, Valentina, 2003. "Strong rules for detecting the number of breaks in a time series," Journal of Econometrics, Elsevier, vol. 117(2), pages 207-244, December.
  14. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
  15. Gombay Edit & Horváth Lajos & Husková Marie, 1996. "Estimators And Tests For Change In Variances," Statistics & Risk Modeling, De Gruyter, vol. 14(2), pages 145-160, February.
  16. 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.
  17. Michael C. Jensen, 1968. "The Performance Of Mutual Funds In The Period 1945–1964," Journal of Finance, American Finance Association, vol. 23(2), pages 389-416, 05.
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