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Fixed Effects Estimation in Panel Nonlinear Fractional Response Models

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  • Xiaoming Li

    (University of Connecticut)

Abstract

Estimations of nonlinear panel models that include individual specific fixed effects are complicated by the incidental parameters problem, that is, the asymptotic bias in the estimation of typical fixed effects panel models generally results in inconsistent estimates. In this paper, I characterize the leading term of a large-T expansion of the biases in the nonlinear least square estimator (NLSE) and estimators of the average partial effects in panel fractional response models. The resulting estimator after analytical bias correction is robust to the incidental parameters bias and reduces the bias order from O(T−1) to O(T−2). I also examine the finite sample performance of the proposed estimator using a new data generating process in which panel fractional response variables are collapsed from repeated, clustered cross-sectional binary probit choices. A proof showing the generated data satisfies the identification assumption at the cluster level has been given. Simulation results suggest that, in the static case, the bias corrected estimator performs comparably to the quasi-maximum likelihood estimator (QMLE), which is the standard approach in the literature, for 8 or more periods, while in the dynamic case, the bias corrected estimators are substantially superior to those QMLE’s.

Suggested Citation

  • Xiaoming Li, 2011. "Fixed Effects Estimation in Panel Nonlinear Fractional Response Models," Working papers 2011-11, University of Connecticut, Department of Economics.
  • Handle: RePEc:uct:uconnp:2011-11
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    References listed on IDEAS

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    1. Fernández-Val, Iván, 2009. "Fixed effects estimation of structural parameters and marginal effects in panel probit models," Journal of Econometrics, Elsevier, vol. 150(1), pages 71-85, May.
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    More about this item

    Keywords

    Fractional responses; Panel Data; Unobserved effects; Probit; Partial effects; Bias; Incidental parameters problem; Fixed effects; Bias Correction;
    All these keywords.

    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
    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid

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