IDEAS home Printed from https://ideas.repec.org/a/wly/japmet/v31y2016i1p281-297.html
   My bibliography  Save this article

Modelling Technical Efficiency in Cross Sectionally Dependent Stochastic Frontier Panels

Author

Listed:
  • Camilla Mastromarco
  • Laura Serlenga
  • Yongcheol Shin

Abstract

No abstract is available for this item.

Suggested Citation

  • Camilla Mastromarco & Laura Serlenga & Yongcheol Shin, 2016. "Modelling Technical Efficiency in Cross Sectionally Dependent Stochastic Frontier Panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 281-297, January.
    • Handle: RePEc:wly:japmet:v:31:y:2016:i:1:p:281-297
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cristina Polo & Julián Ramajo & Alejandro Ricci‐Risquete, 2021. "A stochastic semi‐non‐parametric analysis of regional efficiency in the European Union," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 7-24, February.
    2. Alejandro Puerta & Andr'es Ram'irez-Hassan, 2020. "Inferring hidden potentials in analytical regions: uncovering crime suspect communities in Medell\'in," Papers 2009.05360, arXiv.org.
    3. Camilla Mastromarco & Laura Serlenga & Yongcheol Shin, 2023. "Regional Productivity Network in the EU," CESifo Working Paper Series 10404, CESifo.
    4. Taining Wang & Jinjing Tian & Feng Yao, 2021. "Does high debt ratio influence Chinese firms’ performance? A semiparametric stochastic frontier approach with zero inefficiency," Empirical Economics, Springer, vol. 61(2), pages 587-636, August.
    5. Alberto Gude & Inmaculada Álvarez & Luis Orea, 2018. "Heterogeneous spillovers among Spanish provinces: a generalized spatial stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 50(3), pages 155-173, December.
    6. George Kapetanios & Laura Serlenga & Yongcheol Shin, 2023. "Testing for correlation between the regressors and factor loadings in heterogeneous panels with interactive effects," Empirical Economics, Springer, vol. 64(6), pages 2611-2659, June.
    7. Jacopo Canello & Francesco Vidoli, 2020. "Investigating space‐time patterns of regional industrial resilience through a micro‐level approach: An application to the Italian wine industry," Journal of Regional Science, Wiley Blackwell, vol. 60(4), pages 653-676, September.
    8. Orea, Luis & Álvarez, Inmaculada C., 2019. "A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms," Journal of Econometrics, Elsevier, vol. 213(2), pages 556-577.
    9. Fei Jin & Lung-fei Lee, 2020. "Asymptotic properties of a spatial autoregressive stochastic frontier model," Journal of Spatial Econometrics, Springer, vol. 1(1), pages 1-40, December.
    10. William C. Horrace & Yulong Wang, 2022. "Nonparametric tests of tail behavior in stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 537-562, April.
    11. Efthymios G. Tsionas & Panayotis G. Michaelides, 2016. "A Spatial Stochastic Frontier Model with Spillovers: Evidence for Italian Regions," Scottish Journal of Political Economy, Scottish Economic Society, vol. 63(3), pages 243-257, July.
    12. Pieri, Fabio & Vecchi, Michela & Venturini, Francesco, 2018. "Modelling the joint impact of R&D and ICT on productivity: A frontier analysis approach," Research Policy, Elsevier, vol. 47(9), pages 1842-1852.
    13. Glass, Anthony J. & Kenjegalieva, Karligash & Ajayi, Victor & Adetutu, Morakinyo & Sickles, Robin C., 2016. "Relative Winners and Losers from Efficiency Spillovers in Africa with Policy Implications for Regional Integration," Working Papers 16-003, Rice University, Department of Economics.
    14. Fabio Pieri & Michela Vecchi & Francesco Venturini, 2017. "Modelling the joint impact of R and D and ICT on productivity: A frontier analysis approach," DEM Working Papers 2017/13, Department of Economics and Management.
    15. Arturas Juodis & Simon Reese, 2018. "The Incidental Parameters Problem in Testing for Remaining Cross-section Correlation," Papers 1810.03715, arXiv.org, revised Feb 2021.
    16. Klein, Nadja & Herwartz, Helmut & Kneib, Thomas, 2020. "Modelling regional patterns of inefficiency: A Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales," Journal of Econometrics, Elsevier, vol. 214(2), pages 513-539.
    17. Federico Belotti & Giuseppe Ilardi & Andrea Piano Mortari, 2019. "Estimation of Stochastic Frontier Panel Data Models with Spatial Inefficiency," CEIS Research Paper 459, Tor Vergata University, CEIS, revised 30 May 2019.
    18. Kassoum Ayouba, 2023. "Spatial dependence in production frontier models," Journal of Productivity Analysis, Springer, vol. 60(1), pages 21-36, August.
    19. Katerina Chrysikou & George Kapetanios, 2024. "Heterogeneous Grouping Structures in Panel Data," Papers 2407.19509, arXiv.org.
    20. George Kapetanios & Laura Serlenga & Yongcheol Shin, 2019. "Testing for Correlated Factor Loadings in Cross Sectionally Dependent Panels," SERIES 02-2019, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Jun 2019.
    21. Hung-pin Lai & Kien C. Tran, 2022. "Persistent and transient inefficiency in a spatial autoregressive panel stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 58(1), pages 1-13, August.
    22. Tomohiro Ando & Matthew Greenwood-Nimmo & Yongcheol Shin, 2022. "Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks," Management Science, INFORMS, vol. 68(4), pages 2401-2431, April.
    23. De Vos, Ignace & Stauskas, Ovidijus, 2024. "Cross-section bootstrap for CCE regressions," Journal of Econometrics, Elsevier, vol. 240(1).
    24. Chen, Jia & Shin, Yongcheol & Zheng, Chaowen, 2022. "Estimation and inference in heterogeneous spatial panels with a multifactor error structure," Journal of Econometrics, Elsevier, vol. 229(1), pages 55-79.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:japmet:v:31:y:2016:i:1:p:281-297. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0883-7252/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.