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Linear fixed-effects estimation with nonrepeated outcomes

Author

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  • Helmut Farbmacher
  • Harald Tauchmann

Abstract

We demonstrate that popular linear fixed-effects panel-data estimators are biased and inconsistent when applied in a discrete-time hazard setting, even if the data-generating process is consistent with the linear model. The bias is not just survival bias, but originates from the impossibility to transform the model such that the remaining disturbance term becomes conditional mean independent of the explanatory variables. The bias is hence present even in the absence of unobserved heterogeneity. We discuss instrumental variables estimation, using first-differences of the explanatory variables as instruments, as alternative estimation strategy. Monte Carlo simulations and an empirical application substantiate our theoretical results.

Suggested Citation

  • Helmut Farbmacher & Harald Tauchmann, 2023. "Linear fixed-effects estimation with nonrepeated outcomes," Econometric Reviews, Taylor & Francis Journals, vol. 42(8), pages 635-654, September.
    • Handle: RePEc:taf:emetrv:v:42:y:2023:i:8:p:635-654
    DOI: 10.1080/07474938.2023.2224658
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    Cited by:

    1. Jaka Cepec & Peter Grajzl & Barbara Mörec, 2022. "Public cash and modes of firm exit," Journal of Evolutionary Economics, Springer, vol. 32(1), pages 247-298, January.
    2. Elena Yurkevich & Harald Tauchmann, 2024. "cfbinout and xtdhazard: Control-function estimation of binary-outcome models and the discrete-time hazard model," German Stata Conference 2024 02, Stata Users Group.
    3. Sam Desiere & Bart Cockx, 2021. "How Effective Are Hiring Subsidies to Reduce Long-Term Unemployment among Prime-Aged Jobseekers? Evidence from Belgium," CESifo Working Paper Series 9325, CESifo.
    4. Garcia, Alberto & Heilmayr, Robert, 2024. "Impact evaluation with nonrepeatable outcomes: The case of forest conservation," Journal of Environmental Economics and Management, Elsevier, vol. 125(C).

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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