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Estimation of the threshold stochastic frontier model in the presence of an endogenous sample split variable

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  • Hung-pin Lai

Abstract

Heterogeneity among firms has been an important issue in studying firms’ technical efficiencies. If firms do not randomly fall into different groups with different technologies but by self-selection, statistically it implies the data are subject to the sample selection bias. In this paper, we generalize the stochastic frontier (SF) model to accommodate heterogeneous technologies among firms by considering the threshold SF model with an endogenous threshold variable. We discuss the econometric techniques appropriate for the threshold SF model with panel data. To determine the optimal number of regimes, we use modified the model selection criteria of Gonzalo and Pitarakis (J Econom 110(2):319–352, 2002 ) and investigate their finite sample performance by some Monte Carlo experiments. Finally, we also demonstrate our approach by an empirical example. Copyright Springer Science+Business Media New York 2013

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  • Hung-pin Lai, 2013. "Estimation of the threshold stochastic frontier model in the presence of an endogenous sample split variable," Journal of Productivity Analysis, Springer, vol. 40(2), pages 227-237, October.
    • Handle: RePEc:kap:jproda:v:40:y:2013:i:2:p:227-237
    DOI: 10.1007/s11123-012-0319-6
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    1. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    2. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    3. Efthymios G. Tsionas & Subal C. Kumbhakar, 2004. "Markov switching stochastic frontier model," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 398-425, December.
    4. Mei-Hui Wang & Tai-Hsin Huang, 2009. "Threshold effects of financial status on the cost frontiers of financial institutions in nondynamic panels," Applied Economics, Taylor & Francis Journals, vol. 41(26), pages 3389-3401.
    5. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
    6. Bai, Jushan, 1997. "Estimating Multiple Breaks One at a Time," Econometric Theory, Cambridge University Press, vol. 13(3), pages 315-352, June.
    7. Subal Kumbhakar & Efthymios Tsionas & Timo Sipiläinen, 2009. "Joint estimation of technology choice and technical efficiency: an application to organic and conventional dairy farming," Journal of Productivity Analysis, Springer, vol. 31(3), pages 151-161, June.
    8. Kourtellos, Andros & Stengos, Thanasis & Tan, Chih Ming, 2016. "Structural Threshold Regression," Econometric Theory, Cambridge University Press, vol. 32(4), pages 827-860, August.
    9. Gonzalo, Jesus & Pitarakis, Jean-Yves, 2002. "Estimation and model selection based inference in single and multiple threshold models," Journal of Econometrics, Elsevier, vol. 110(2), pages 319-352, October.
    10. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    11. Pitarakis Jean-Yves, 2006. "Model Selection Uncertainty and Detection of Threshold Effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(1), pages 1-30, March.
    12. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    13. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    14. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
    15. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    16. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    17. Yélou, Clément & Larue, Bruno & Tran, Kien C., 2010. "Threshold effects in panel data stochastic frontier models of dairy production in Canada," Economic Modelling, Elsevier, vol. 27(3), pages 641-647, May.
    18. Luis Orea & Subal C. Kumbhakar, 2004. "Efficiency measurement using a latent class stochastic frontier model," Empirical Economics, Springer, vol. 29(1), pages 169-183, January.
    19. Antonio Alvarez & Julio del Corral, 2010. "Identifying different technologies using a latent class model: extensive versus intensive dairy farms," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 37(2), pages 231-250, June.
    20. Kumbhakar, Subal C. & Park, Byeong U. & Simar, Leopold & Tsionas, Efthymios G., 2007. "Nonparametric stochastic frontiers: A local maximum likelihood approach," Journal of Econometrics, Elsevier, vol. 137(1), pages 1-27, March.
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    1. Badunenko, Oleg & D’Inverno, Giovanna & De Witte, Kristof, 2023. "On distinguishing the direct causal effect of an intervention from its efficiency-enhancing effects," European Journal of Operational Research, Elsevier, vol. 310(1), pages 432-447.
    2. Hung-pin Lai, 2015. "Maximum likelihood estimation of the stochastic frontier model with endogenous switching or sample selection," Journal of Productivity Analysis, Springer, vol. 43(1), pages 105-117, February.
    3. Chen, Jun & King, Tao-Hsien Dolly & Wen, Min-Ming, 2015. "Do joint ventures and strategic alliances create value for bondholders?," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 247-267.
    4. Parmeter, Christopher F., 2021. "Is it MOLS or COLS?," Efficiency Series Papers 2021/04, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    5. Efthymios G. Tsionas & Kien C. Tran & Panayotis G. Michaelides, 2019. "Bayesian inference in threshold stochastic frontier models," Empirical Economics, Springer, vol. 56(2), pages 399-422, February.

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    More about this item

    Keywords

    Stochastic frontier model; Endogeneity; Threshold; Panel data; Fixed effects; C24; C52; R3;
    All these keywords.

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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