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Stochastic production function estimation: small sample properties of ML versus FGLS

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  • Atanu Saha
  • Arthur Havenner
  • Hovav Talpaz

Abstract

Just-Pope production functions have been traditionally estimated by feasible generalized least squares (FGLS). This paper investigates the small-sample properties of FGLS and maximum likelihood (ML) estimators in heteroscedastic error models. Monte Carlo experiment results show that in small samples, even when the error distribution departs significantly from normality, the ML estimator is more efficient and suffers from less bias than FGLS. Importantly, FGLS was found to seriously understate the risk effects of inputs and provide biased marginal product estimates. These results are explained by showing that the FGLS criteria being optimized at the multiple stages are not logically consistent.

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  • Atanu Saha & Arthur Havenner & Hovav Talpaz, 1997. "Stochastic production function estimation: small sample properties of ML versus FGLS," Applied Economics, Taylor & Francis Journals, vol. 29(4), pages 459-469.
    • Handle: RePEc:taf:applec:v:29:y:1997:i:4:p:459-469
    DOI: 10.1080/000368497326958
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    3. De Nova, Carolina Carbajal, 2021. "Synthetic data. A novel proposed method for applied risk management," 95th Annual Conference, March 29-30, 2021, Warwick, UK (Hybrid) 311085, Agricultural Economics Society - AES.
    4. Chen, Po-Chi & Yu, Ming-Miin & Chang, Ching-Cheng & Hsu, Shih-Hsun, 2008. "Total factor productivity growth in China's agricultural sector," China Economic Review, Elsevier, vol. 19(4), pages 580-593, December.
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    6. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
    7. Chang, Hung-Hao & Boisvert, Richard N., 2009. "Does Participation in the Conservation Reserve Program and/or Off-Farm Work Affect the Level and Distribution of Farm Household Income?," Working Papers 57035, Cornell University, Department of Applied Economics and Management.
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    10. Ragner Tveterås & G. H. Wan, 2000. "Flexible panel data models for risky production technologies with an application to salmon aquaculture," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 367-389.
    11. Catherine Benjamin & Ewen Gallic, 2017. "Effects of Climate Change on Agriculture: a European case study," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 2017-16, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
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    15. Asche, Frank & Tveteras, Ragnar, 1999. "Modeling Production Risk With A Two-Step Procedure," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 24(2), pages 1-16, December.
    16. K. Palanisami & C. R. Ranganathan & K. R. Kakumanu & Udaya Sekhar Nagothu, 2011. "A Hybrid Model to Quantify the Impact of Climate Change on Agriculture in Godavari Basin, India," Energy and Environment Research, Canadian Center of Science and Education, vol. 1(1), pages 1-32, December.
    17. Mohamed Adel Dhif & Mohamed Mekki Ben Jemaa, 2004. "Uncertainty and Risk Aversion: Implication for Tunisian Cereals Crops," Working Papers 0415, Economic Research Forum, revised 07 Jan 2004.
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