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Dynamic Structural Econometric Modeling of the Ethanol Industry

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Modeling, Dynamics, Optimization and Bioeconomics II (DGS 2014)

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Abstract

This chapter reviews some of the papers my co-authors and I have written developing and estimating dynamic structural econometric models of dynamic games in the ethanol industry. These structural econometric models model the dynamic and strategic decisions made by ethanol firms and enable us to analyze the effects of government policy. Analyses that ignore the dynamic implications of government policies, including their effects on incumbent ethanol firms’ investment, production, and exit decisions and on potential entrants’ entry behavior, may generate incomplete estimates of the impact of the policies and misleading predictions of the future evolution of the fuel ethanol industry. In Thome and Lin Lawell [50], we estimate a model of the investment timing game in corn ethanol plants in the United States. In Yi and Lin Lawell [53, 54], we estimate a model of the investment timing game in ethanol plants worldwide that allows for the choice among different feedstocks. In Yi et al. [55], we estimate a structural econometric model of ethanol firms’ investment, production, entry, and exit decisions in order to analyze the effects of government subsidies and the Renewable Fuel Standard on the U.S. fuel ethanol industry. The results of our research will help determine which policies and factors can promote fuel-ethanol industry development.

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Author information

Authors and Affiliations

  1. Cornell University, Ithaca, NY, USA

    C.-Y. Cynthia Lin Lawell

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  1. C.-Y. Cynthia Lin Lawell
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Correspondence to C.-Y. Cynthia Lin Lawell .

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Editors and Affiliations

  1. Department of Mathematics and LIAAD-INESC TEC, University of Porto, Porto, Portugal

    Alberto A. Pinto

  2. Department of Agricultural and Resource Economics, University of California, Berkeley, California, USA

    David Zilberman

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Lin Lawell, CY.C. (2017). Dynamic Structural Econometric Modeling of the Ethanol Industry. In: Pinto, A., Zilberman, D. (eds) Modeling, Dynamics, Optimization and Bioeconomics II. DGS 2014. Springer Proceedings in Mathematics & Statistics, vol 195. Springer, Cham. https://doi.org/10.1007/978-3-319-55236-1_14

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