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Consistent inference in fixed-effects stochastic frontier models

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  • Belotti, Federico
  • Ilardi, Giuseppe

Abstract

The classical stochastic frontier panel data models provide no mechanism to disentangle individual time invariant unobserved heterogeneity from inefficiency. Greene (2005a, b) proposed the so-called “true” fixed-effects specification that distinguishes these two latent components. However, due to the incidental parameters problem, his maximum likelihood estimator may lead to biased variance estimates. We propose two alternative estimators that achieve consistency for n→∞ with fixed T. Furthermore, we extend the Chen et al. (2014) results providing a feasible estimator when the inefficiency is heteroskedastic and follows a first-order autoregressive process. We investigate the behavior of the proposed estimators through Monte Carlo simulations showing good finite sample properties, especially in small samples. An application to hospitals’ technical efficiency illustrates the usefulness of the new approach.

Suggested Citation

  • Belotti, Federico & Ilardi, Giuseppe, 2018. "Consistent inference in fixed-effects stochastic frontier models," Journal of Econometrics, Elsevier, vol. 202(2), pages 161-177.
  • Handle: RePEc:eee:econom:v:202:y:2018:i:2:p:161-177
    DOI: 10.1016/j.jeconom.2017.09.005
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    Cited by:

    1. repec:spr:jglont:v:8:y:2018:i:1:d:10.1186_s40497-018-0101-y is not listed on IDEAS
    2. Paul, Satya & Shankar, Sriram, 2018. "Modelling Efficiency Effects in a True Fixed Effects Stochastic Frontier," MPRA Paper 87437, University Library of Munich, Germany.
    3. Lai, Hung-pin & Kumbhakar, Subal C., 2018. "Estimation of Dynamic Stochastic Frontier Model using Likelihood-based Approaches," MPRA Paper 87830, University Library of Munich, Germany.
    4. repec:eee:eneeco:v:72:y:2018:i:c:p:166-176 is not listed on IDEAS

    More about this item

    Keywords

    Stochastic frontiers; Fixed-effects; Panel data; Marginal simulated likelihood; Pairwise differencing;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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