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Inference on time-invariant variables using panel data: a pretest estimator

Author

Listed:
  • Jean-Bernard Chatelain

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Kirsten Ralf

    (INSEEC - Institut des hautes études économiques et commerciales | School of Business and Economics, ESCEM Tours Poitiers - ESCEM School of Business and Management - Groupe école supérieure de commerce et de management Tours-Poitiers)

Abstract

For panel data models including time-invariant variables, this paper proposes a new Hausman pretest estimator of the internal instruments of Hausman-Taylor estimator. It assumes Mundlak and Krishnakumar linear specification for the endogeneity of random individual effects. Furthermore, the paper evaluates the biases of currently used estimators: repeated between, ordinary least squares, two-stage restricted between, Oaxaca-Geisler estimator, fixed effect vector decomposition, and generalized least squares. Some of these may lead to erroneous conclusions regarding the statistical significance of the estimated parameter values of time-invariant variables, especially when time-invariant variables are correlated with the individual effects.

Suggested Citation

  • Jean-Bernard Chatelain & Kirsten Ralf, 2020. "Inference on time-invariant variables using panel data: a pretest estimator," PSE Working Papers halshs-03059883, HAL.
    • Handle: RePEc:hal:psewpa:halshs-03059883
    DOI: 10.2139/ssrn.3165633
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    Cited by:

    1. Ullah, Inayat & Hussain, Saqib, 2023. "Impact of early access to land record information through digitization: Evidence from Alternate Dispute Resolution Data in Punjab, Pakistan," Land Use Policy, Elsevier, vol. 134(C).
    2. Oscar Díaz Olariaga & Carlos Alonso‐Malaver, 2022. "Impact of airport policies on regional development. Evidence from the Colombian case," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(6), pages 185-210, December.
    3. Chiara Burlina & Andrés Rodríguez-Pose, 2023. "Alone and lonely. The economic cost of solitude for regions in Europe," Environment and Planning A, , vol. 55(8), pages 2067-2087, November.
    4. Yang, Dianyi & Huang, Leike, 2024. "A Reproduction of "Do Female Officers Police Differently? Evidence from Traffic Stops" (American Journal of Political Science, 2021)," I4R Discussion Paper Series 127, The Institute for Replication (I4R).
    5. Barkley, Andrew, . "Regional Spillovers in Wheat Variety Selection: Kansas Wheat Breeding and Variety Adoption," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 50(3).
    6. Anti, Sebastian & Zhang, Zhihui, 2023. "Roads, women’s employment, and gender equity: Evidence from Cambodia," World Development, Elsevier, vol. 171(C).

    More about this item

    Keywords

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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