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Estimation of a level shift in panel data with fractionally integrated errors

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  • Chang, Seong Yeon

Abstract

This article deals with the estimation of a common break point in panel data. We consider the general case of fractionally integrated errors with memory parameter d∈(−0.5,0.5) and establish the consistency, convergence rate, and limiting distribution of the estimated common break point. The ordinary least squares method is used for estimating the break point in mean. We find that the convergence rate is invariant to the order of fractional integration. Simulation experiments are provided to illustrate some of the theoretical results.

Suggested Citation

  • Chang, Seong Yeon, 2021. "Estimation of a level shift in panel data with fractionally integrated errors," Economics Letters, Elsevier, vol. 206(C).
  • Handle: RePEc:eee:ecolet:v:206:y:2021:i:c:s0165176521002482
    DOI: 10.1016/j.econlet.2021.109971
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    References listed on IDEAS

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    1. Jushan Bai, 1994. "Least Squares Estimation Of A Shift In Linear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(5), pages 453-472, September.
    2. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    3. Hsu, Yu-Chin & Kuan, Chung-Ming, 2008. "Change-point estimation of nonstationary I(d) processes," Economics Letters, Elsevier, vol. 98(2), pages 115-121, February.
    4. Robinson, P.M., 2005. "The distance between rival nonstationary fractional processes," Journal of Econometrics, Elsevier, vol. 128(2), pages 283-300, October.
    5. Badi H. Baltagi & Chihwa Kao & Long Liu, 2017. "Estimation and identification of change points in panel models with nonstationary or stationary regressors and error term," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 85-102, March.
    6. Perron, Pierre & Zhu, Xiaokang, 2005. "Structural breaks with deterministic and stochastic trends," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 65-119.
    7. Bai, Jushan, 1998. "A Note On Spurious Break," Econometric Theory, Cambridge University Press, vol. 14(5), pages 663-669, October.
    8. 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.
    9. Chung‐Ming Kuan & Chih‐Chiang Hsu, 1998. "Change‐Point Estimation of Fractionally Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(6), pages 693-708, November.
    10. Bai, Jushan, 2010. "Common breaks in means and variances for panel data," Journal of Econometrics, Elsevier, vol. 157(1), pages 78-92, July.
    11. Wang, Qiying & Lin, Yan-Xia & Gulati, Chandra M., 2003. "Asymptotics For General Fractionally Integrated Processes With Applications To Unit Root Tests," Econometric Theory, Cambridge University Press, vol. 19(1), pages 143-164, February.
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    More about this item

    Keywords

    Change points; Common breaks; Fractional processes; Level shifts; Panel data; Structural breaks;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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