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Detection of structural breaks in linear dynamic panel data models

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  • De Wachter, Stefan
  • Tzavalis, Elias

Abstract

A break detection testing procedure for the well-known AR(p) linear panel data model with exogenous or pre-determined regressors is developed. The proposed method can accommodate a structural break in the slope parameters as well as in the fixed effects. Breaks in the latter are not constrained by any type of cross-sectional homogeneity and are allowed to be correlated with all past information. Monte Carlo simulations indicate that the test performs satisfactorily even in the type of panel datasets with short time-dimension often encountered in practice. As an empirical illustration, the paper implements the test to detect the effects of the 1997 Asian crisis on the investment decisions of Asian companies.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 56 (2012)
Issue (Month): 11 ()
Pages: 3020-3034

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Handle: RePEc:eee:csdana:v:56:y:2012:i:11:p:3020-3034

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Web page: http://www.elsevier.com/locate/csda

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Keywords: Panel data; Structural breaks; Break detection;

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  1. De Wachter, Stefan & Tzavalis, Elias, 2005. "Monte Carlo comparison of model and moment selection and classical inference approaches to break detection in panel data models," Economics Letters, Elsevier, vol. 88(1), pages 91-96, July.
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Citations

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Cited by:
  1. Felix Chan Tommaso Mancini-Griffoli Laurent L. Pauwels, 2006. "Stability Tests for Heterogeneous Panel Data," IHEID Working Papers 24-2006, Economics Section, The Graduate Institute of International Studies, revised Dec 2006.
  2. Ryan Brady, 2013. "The Spatial Diffusion of Regional Housing Prices across U.S. States," Departmental Working Papers 45, United States Naval Academy Department of Economics.
  3. De Wachter, Stefan & Tzavalis, Elias, 2005. "Monte Carlo comparison of model and moment selection and classical inference approaches to break detection in panel data models," Economics Letters, Elsevier, vol. 88(1), pages 91-96, July.
  4. Chihwa Kao & Lorenzo Trapani & Giovanni Urga, 2007. "Modelling and Testing for Structural Changes in Panel Cointegration Models with Common and Idiosyncratic Stochastic Trends," Working Papers 0708, Department of Economics and Technology Management, University of Bergamo.
  5. Karavias, Yiannis & Tzavalis, Elias, 2014. "Testing for unit roots in short panels allowing for a structural break," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 391-407.
  6. Kim, Dukpa, 2011. "Estimating a common deterministic time trend break in large panels with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 164(2), pages 310-330, October.
  7. Fay Dunkerley & Amihai Glazer & Stef Proost, 2010. "What Drives Gasoline Prices?," Working Papers 091005, University of California-Irvine, Department of Economics.
  8. Bada, Oualid & Kneip, Alois, 2014. "Parameter cascading for panel models with unknown number of unobserved factors: An application to the credit spread puzzle," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 95-115.
  9. Sonja Keller & Ashoka Mody, 2010. "International Pricing of Emerging Market Corporate Debt," IMF Working Papers 10/26, International Monetary Fund.
  10. Karavias, Yiannis & Tzavalis, Elias, 2012. "Generalized �Fixed-T Panel Unit Root Tests Allowing for Structural Breaks," MPRA Paper 43128, University Library of Munich, Germany.
  11. Bai, Jushan, 2010. "Common breaks in means and variances for panel data," Journal of Econometrics, Elsevier, vol. 157(1), pages 78-92, July.

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