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A spatial autoregressive stochastic frontier model for panel data incorporating a model of technical inefficiency

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  • Tsukamoto, Takahiro

Abstract

By integrating Battese and Coelli’s (1995) model and the spatial autoregressive model (SAR), a spatial autoregressive stochastic frontier model for panel data is developed. The main feature of this frontier model is a spatial lag term of explained variables and the joint structure of a production possibility frontier with a model of technical inefficiency. The model addresses both spatial dependence and heteroskedastic technical inefficiency. This study applies maximum likelihood methods considering the endogenous spatial lag term. The proposed model nests several existing models. Further, an empirical analysis using data on the Japanese manufacturing industry is conducted and the existing models are tested against the proposed model, which is found to be statistically supported. The findings suggest that estimates in the existing spatial and non-spatial models may exhibit bias because of lack of determinants of technical inefficiency, as well as a spatial lag. This bias also affects the technical efficiency score and its ranking.

Suggested Citation

  • Tsukamoto, Takahiro, 2019. "A spatial autoregressive stochastic frontier model for panel data incorporating a model of technical inefficiency," Japan and the World Economy, Elsevier, vol. 50(C), pages 66-77.
    • Handle: RePEc:eee:japwor:v:50:y:2019:i:c:p:66-77
    DOI: 10.1016/j.japwor.2018.11.003
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    Citations

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    Cited by:

    1. Rouven E. Haschka & Helmut Herwartz & Clara Silva Coelho & Yabibal M. Walle, 2023. "The impact of local financial development and corruption control on firm efficiency in Vietnam: evidence from a geoadditive stochastic frontier analysis," Journal of Productivity Analysis, Springer, vol. 60(2), pages 203-226, October.
    2. Laureti, Tiziana & Benedetti, Ilaria & Branca, Giacomo, 2021. "Water use efficiency and public goods conservation: A spatial stochastic frontier model applied to irrigation in Southern Italy," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    3. Patel, Pankaj C. & Tsionas, Mike G., 2022. "Cultural interconnectedness in supply chain networks and change in performance: An internal efficiency perspective," International Journal of Production Economics, Elsevier, vol. 243(C).
    4. Lamees Al-Durgham & Mohammad Adeinat, 2020. "Efficiency of Listed Manufacturing Firms in Jordan: A Stochastic Frontier Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 10(6), pages 5-9.
    5. Kassoum Ayouba, 2023. "Spatial dependence in production frontier models," Journal of Productivity Analysis, Springer, vol. 60(1), pages 21-36, August.
    6. Bernini, Cristina & Galli, Federica, 2023. "Innovation, productivity and spillover effects in the Italian accommodation industry," Economic Modelling, Elsevier, vol. 119(C).
    7. Samuel Faria & Sofia Gouveia & Alexandre Guedes & João Rebelo, 2021. "Transient and Persistent Efficiency and Spatial Spillovers: Evidence from the Portuguese Wine Industry," Economies, MDPI, vol. 9(3), pages 1-20, August.

    More about this item

    Keywords

    Stochastic frontier analysis (SFA); Determinants of technical inefficiency; Spatial autoregressive dependence; Japanese manufacturing industry;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production

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