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Estimation of a panel stochastic frontier model with unobserved common shocks

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  • Hsu, Chih-Chiang
  • Lin, Chang-Ching
  • Yin, Shou-Yung

Abstract

This paper develops panel stochastic frontier models with unobserved common correlated effects. The common correlated effects provide a way of modeling cross-sectional dependence and represent heterogeneous impacts on individuals resulting from unobserved common shocks. Traditional panel stochastic frontier models do not distinguish between common correlated effects and technical inefficiency. In this paper, we propose a modified maximum likelihood estimator (MLE) that does not require estimating unobserved common correlated effects. We show that the proposed method can control the common correlated effects and obtain consistent estimates of parameters and technical efficiency for the panel stochastic frontier model. Our Monte Carlo simulations show that the modified MLE has satisfactory finite sample properties under a significant degree of cross-sectional dependence for relatively small T. The proposed method is also illustrated in applications based on a cross country comparison of the efficiency of banking industries.

Suggested Citation

  • Hsu, Chih-Chiang & Lin, Chang-Ching & Yin, Shou-Yung, 2012. "Estimation of a panel stochastic frontier model with unobserved common shocks," MPRA Paper 37313, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:37313
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    References listed on IDEAS

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    Cited by:

    1. Massimo Filippini & Elisa Tosetti, 2014. "Stochastic Frontier Models for Long Panel Data Sets: Measurement of the Underlying Energy Efficiency for the OECD Countries," CER-ETH Economics working paper series 14/198, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.

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    More about this item

    Keywords

    fixed effects; common correlated effects; factor structure; cross-sectional dependence; stochastic frontier;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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