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Test of Hypotheses in a Time Trend Panel Data Model with Serially Correlated Error Component Disturbances

In: Essays in Honor of Peter C. B. Phillips

Author

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  • Badi H. Baltagi
  • Chihwa Kao
  • Long Liu

Abstract

This paper studies test of hypotheses for the slope parameter in a linear time trend panel data model with serially correlated error component disturbances. We propose a test statistic that uses a bias corrected estimator of the serial correlation parameter. The proposed test statistic which is based on the corresponding fixed effects feasible generalized least squares (FE-FGLS) estimator of the slope parameter has the standard normal limiting distribution which is valid whether the remainder error is I(0) or I(1). This performs well in Monte Carlo experiments and is recommended.

Suggested Citation

  • Badi H. Baltagi & Chihwa Kao & Long Liu, 2014. "Test of Hypotheses in a Time Trend Panel Data Model with Serially Correlated Error Component Disturbances," Advances in Econometrics, in: Essays in Honor of Peter C. B. Phillips, volume 33, pages 347-394, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320140000033011
    DOI: 10.1108/S0731-905320140000033011
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    Cited by:

    1. Badi H. Baltagi & Chihwa Kao & Long Liu, 2020. "Testing for shifts in a time trend panel data model with serially correlated error component disturbances," Econometric Reviews, Taylor & Francis Journals, vol. 39(8), pages 745-762, September.
    2. Chihwa Kao & Long Liu & Rui Sun, 2021. "A bias-corrected fixed effects estimator in the dynamic panel data model," Empirical Economics, Springer, vol. 60(1), pages 205-225, January.
    3. Li, Qi & Sarafidis, Vasilis & Westerlund, Joakim, 2020. "Essays in Honor of Professor Badi H Baltagi: Editorial," MPRA Paper 104751, University Library of Munich, Germany.

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    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

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