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Adjustment and unobserved heterogeneity in dynamic stochastic frontier models

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  • Grigorios Emvalomatis

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  • Grigorios Emvalomatis, 2012. "Adjustment and unobserved heterogeneity in dynamic stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 37(1), pages 7-16, February.
    • Handle: RePEc:kap:jproda:v:37:y:2012:i:1:p:7-16
    DOI: 10.1007/s11123-011-0217-3
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    1. Kumbhakar, Subal C., 1991. "Estimation of technical inefficiency in panel data models with firm- and time-specific effects," Economics Letters, Elsevier, vol. 36(1), pages 43-48, May.
    2. Florian Heiss, 2008. "Sequential numerical integration in nonlinear state space models for microeconometric panel data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 373-389.
    3. Efthymios G. Tsionas, 2006. "Inference in dynamic stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 669-676, July.
    4. Rungsuriyawiboon, Supawat & Stefanou, Spiro E., 2007. "Dynamic Efficiency Estimation: An Application to U.S. Electric Utilities," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 226-238, April.
    5. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    6. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    7. Ivaldi, Marc & Monier-Dilhan, Sylvette & Simioni, Michel, 1995. "Stochastic production frontiers and panel data: A latent variable framework," European Journal of Operational Research, Elsevier, vol. 80(3), pages 534-547, February.
    8. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    9. Seung Ahn & Young Lee & Peter Schmidt, 2007. "Stochastic frontier models with multiple time-varying individual effects," Journal of Productivity Analysis, Springer, vol. 27(1), pages 1-12, February.
    10. Jacek Osiewalski & Mark Steel, 1998. "Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 10(1), pages 103-117, July.
    11. 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.
    12. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    13. Seung Ahn & Robin Sickles, 2000. "Estimation of long-run inefficiency levels: a dynamic frontier approach," Econometric Reviews, Taylor & Francis Journals, vol. 19(4), pages 461-492.
    14. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    15. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    16. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
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    Citations

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    Cited by:

    1. Skevas, Ioannis, 2020. "Inference in the spatial autoregressive efficiency model with an application to Dutch dairy farms," European Journal of Operational Research, Elsevier, vol. 283(1), pages 356-364.
    2. Liu, Xiao-Yan & Pollitt, Michael G. & Xie, Bai-Chen & Liu, Li-Qiu, 2019. "Does environmental heterogeneity affect the productive efficiency of grid utilities in China?," Energy Economics, Elsevier, vol. 83(C), pages 333-344.
    3. Rahmatallah Poudineh & Grigorios Emvalomatis & Tooraj Jamasb, 2014. "Dynamic Efficiency and Incentive Regulation: An Application to Electricity Distribution Networks," Working Papers EPRG 1402, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    4. Jorge E. Galán & Michael G. Pollitt, 2014. "Inefficiency persistence and heterogeneity in Colombian electricity distribution utilities," Cambridge Working Papers in Economics 1423, Faculty of Economics, University of Cambridge.
    5. Hung‐pin Lai & Subal C. Kumbhakar, 2020. "Estimation of a dynamic stochastic frontier model using likelihood‐based approaches," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 217-247, March.
    6. Baños-Pino, José F. & Boto-García, David & Zapico, Emma, 2021. "Persistence and dynamics in the efficiency of toll motorways: The Spanish case," Efficiency Series Papers 2021/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    7. Anne Musson & Damien Rousselière, 2020. "Exploring the effect of crisis on cooperatives: a Bayesian performance analysis of French craftsmen cooperatives," Applied Economics, Taylor & Francis Journals, vol. 52(25), pages 2657-2678, May.
    8. Belotti, Federico & Ilardi, Giuseppe, 2018. "Consistent inference in fixed-effects stochastic frontier models," Journal of Econometrics, Elsevier, vol. 202(2), pages 161-177.
    9. Baños-Pino, José F. & Boto-García, David & Zapico, Emma, 2022. "Persistence and dynamics in the efficiency of toll motorways: The Spanish case," Economics of Transportation, Elsevier, vol. 31(C).
    10. Galán, Jorge E. & Veiga, Helena & Wiper, Michael P., 2015. "Dynamic effects in inefficiency: Evidence from the Colombian banking sector," European Journal of Operational Research, Elsevier, vol. 240(2), pages 562-571.
    11. Ioannis Skevas & Grigorios Emvalomatis & Bernhard Brümmer, 2018. "The effect of farm characteristics on the persistence of technical inefficiency: a case study in German dairy farming," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(1), pages 3-25.
    12. Iordanis Parikoglou & Grigorios Emvalomatis & Fiona Thorne, 2022. "Precision livestock agriculture and productive efficiency: The case of milk recording in Ireland," Agricultural Economics, International Association of Agricultural Economists, vol. 53(S1), pages 109-120, November.
    13. Stefano Mainardi, 2021. "Parametric and Semiparametric Efficiency Frontiers in Fishery Analysis: Overview and Case Study on the Falkland Islands," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 79(2), pages 169-210, June.
    14. Skevas, Ioannis & Emvalomatis, Grigorios & Brümmer, Bernhard, 2018. "Productivity growth measurement and decomposition under a dynamic inefficiency specification: The case of German dairy farms," European Journal of Operational Research, Elsevier, vol. 271(1), pages 250-261.
    15. Andrews Antony & Emvalomatis Grigorios, 2024. "Efficiency Measurement in Healthcare: The Foundations, Variables, and Models – A Narrative Literature Review," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 18(1), pages 1-24.
    16. Huang, Yu-Fan & Luo, Sui & Wang, Hung-Jen, 2018. "Flexible panel stochastic frontier model with serially correlated errors," Economics Letters, Elsevier, vol. 163(C), pages 55-58.
    17. Jean Joseph Minviel & Timo Sipiläinen, 2021. "A dynamic stochastic frontier approach with persistent and transient inefficiency and unobserved heterogeneity," Agricultural Economics, International Association of Agricultural Economists, vol. 52(4), pages 575-589, July.
    18. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.
    19. Lachaud, Michée A. & Bravo-Ureta, Boris E., 2022. "A Bayesian statistical analysis of return to agricultural R&D investment in Latin America: Implications for food security," Technology in Society, Elsevier, vol. 70(C).
    20. Antony Andrews, 2021. "Persistence of Cost Inefficiency Among Schools: A Myth or Reality?," Economic Papers, The Economic Society of Australia, vol. 40(1), pages 73-77, March.
    21. Galán, Jorge E. & Pollitt, Michael G., 2014. "Inefficiency persistence and heterogeneity in Colombian electricity utilities," Energy Economics, Elsevier, vol. 46(C), pages 31-44.
    22. repec:cte:wsrepe:ws131918 is not listed on IDEAS
    23. Jean Joseph Minviel & Timo Sipiläinen, 2018. "Dynamic stochastic analysis of the farm subsidy-efficiency link: evidence from France," Journal of Productivity Analysis, Springer, vol. 50(1), pages 41-54, October.
    24. A. G. Billé & C. Salvioni & R. Benedetti, 2018. "Modelling spatial regimes in farms technologies," Journal of Productivity Analysis, Springer, vol. 49(2), pages 173-185, June.

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    More about this item

    Keywords

    Dynamic stochastic frontier; Autocorrelated inefficiency; Panel data; Adjustment costs; D24; D21; C23;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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