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Simulated Maximum Likelihood Estimation Based On First-Order Conditions

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  • Michael P. Keane

Abstract

I describe a strategy for structural estimation that uses simulated maximum likelihood (SML) to estimate the structural parameters appearing in a model's first-order conditions (FOCs). Generalized method of moments (GMM) is often the preferred method for estimation of FOCs, as it avoids distributional assumptions on stochastic terms, "provided" all structural errors enter the FOCs additively, giving a single composite additive error. But SML has advantages over GMM in models where multiple structural errors enter the FOCs nonadditively. I develop new simulation algorithms required to implement SML based on FOCs, and I illustrate the method using a model of U.S. multinational corporations. Copyright © (2009) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Suggested Citation

  • Michael P. Keane, 2009. "Simulated Maximum Likelihood Estimation Based On First-Order Conditions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(2), pages 627-675, May.
    • Handle: RePEc:ier:iecrev:v:50:y:2009:i:2:p:627-675
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    Cited by:

    1. Ledic, Marko, 2012. "Estimating Labor Supply at the Extensive Margin in the presence of Sample Selection Bias," MPRA Paper 55745, University Library of Munich, Germany.
    2. Michael P. Keane, 2011. "Labor Supply and Taxes: A Survey," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 961-1075, December.
    3. Carpentier, Alain & Gohin, Alexandre & Sckokai, Paolo & Thomas, Alban, 2015. "Economic modelling of agricultural production: past advances and new challenges," Revue d'Etudes en Agriculture et Environnement, Editions NecPlus, vol. 96(01), pages 131-165, March.
    4. Hall, George & Rust, John, 2021. "Estimation of endogenously sampled time series: The case of commodity price speculation in the steel market," Journal of Econometrics, Elsevier, vol. 222(1), pages 219-243.
    5. Koutchad, P. & Carpentier, A. & Femenia, F., 2018. "Dealing with corner solutions in multi-crop micro-econometric models: an endogenous regime approach with regime fixed costs," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277530, International Association of Agricultural Economists.
    6. Koutchadé, Philippe & Carpentier, Alain & Féménia, Fabienne, 2015. "Empirical modelling of production decisions of heterogeneous farmers with mixed models," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205098, Agricultural and Applied Economics Association.
    7. Koutchade, Philippe & Carpentier, Alain & Femenia, Fabienne, 2015. "Accounting for unobserved heterogeneity in micro-econometric agricultural production models: a random parameter approach," 2015 Conference, August 9-14, 2015, Milan, Italy 212015, International Association of Agricultural Economists.
    8. Koutchade, Philippe & Carpentier, Alain & Féménia, Fabienne, 2015. "Empirical modeling of production decisions of heterogeneous farmers with random parameter models," Working Papers 210097, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
    9. Obafèmi P. Koutchadé & Alain Carpentier & Fabienne Femenia, 2021. "Modeling Corners, Kinks, and Jumps in Crop Acreage Choices: Impacts of the EU Support to Protein Crops," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(4), pages 1502-1524, August.

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