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Impact of liquidity risk on variations in efficiency and productivity: A panel gamma simulated maximum likelihood estimation

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  • Shaik, Saleem

Abstract

The objective of this study is to assess the importance of short- and long-run liquidity or debt risk on technical inefficiency and productivity. An alternative panel estimator of normal-gamma stochastic frontier model is proposed using a simulated maximum likelihood estimation technique. Empirical estimates indicate a difference in the parameter coefficients of gamma stochastic production function, and heterogeneity function variables between the pooled and the Swamy–Arora panel models. The results from this study show short and long run risk or variations in liquidity or debt-servicing ratio play an important role in explaining the variance in efficiency and productivity.

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  • Shaik, Saleem, 2015. "Impact of liquidity risk on variations in efficiency and productivity: A panel gamma simulated maximum likelihood estimation," European Journal of Operational Research, Elsevier, vol. 245(2), pages 463-469.
    • Handle: RePEc:eee:ejores:v:245:y:2015:i:2:p:463-469
    DOI: 10.1016/j.ejor.2015.03.018
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    Cited by:

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    2. Jin, Man & Zhao, Shunan & Kumbhakar, Subal C., 2019. "Financial constraints and firm productivity: Evidence from Chinese manufacturing," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1139-1156.
    3. Ioannis E. Tsolas, 2021. "Efficiency and Determinants of Capital Structure in the Greek Pharmaceutical, Cosmetic and Detergent Industries," JRFM, MDPI, vol. 14(12), pages 1-13, December.
    4. Liyun Zhu & Alfons Oude Lansink, 2022. "Dynamic sustainable productivity growth of Dutch dairy farming," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-19, February.
    5. Feuerriegel, Stefan & Gordon, Julius, 2019. "News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions," European Journal of Operational Research, Elsevier, vol. 272(1), pages 162-175.
    6. Sakouvogui Kekoura & Shaik Saleem & Doetkott Curt & Magel Rhonda, 2021. "Sensitivity analysis of stochastic frontier analysis models," Monte Carlo Methods and Applications, De Gruyter, vol. 27(1), pages 71-90, March.

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