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Iclogit: a Stata module for estimating a mixed logit model with discrete mixing distribution via the Expectation-Maximization algorithm

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  • Daniele Pacifico
  • Hong il Yoo

Abstract

This paper describe Iclogit, a Stata module to fit latent class logit models through the Expectation-Maximization algorithm. The stability of this estimation method allows overcoming some of the computational difficulties that normally arise when fitting such models with many latent classes. This, in turn, permits users to estimate nonparameterically the mixing distribution of the random coefficients because the more the mass points of the latent class model, the better the approximation of the unknown joint density of the random coefficients.

Suggested Citation

  • Daniele Pacifico & Hong il Yoo, 2012. "Iclogit: a Stata module for estimating a mixed logit model with discrete mixing distribution via the Expectation-Maximization algorithm," Working Papers 6, Department of the Treasury, Ministry of the Economy and of Finance.
    • Handle: RePEc:itt:wpaper:2012-6
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    References listed on IDEAS

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    1. Joel Huber and Kenneth Train., 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Economics Working Papers E00-289, University of California at Berkeley.
    2. Daniele Pacifico, 2010. "Estimating nonparametric mixed logit models via EM algorithm," Center for the Analysis of Public Policies (CAPP) 0072, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
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    Full references (including those not matched with items on IDEAS)

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    11. Saint-Cyr, Legrand D. F., 2017. "Farm heterogeneity and agricultural policy impacts on size dynamics: evidence from France," Working Papers 258013, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
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    15. Dolores Garrido & Rosa Karina Gallardo, 2022. "Are improvements in convenience good enough for consumers to prefer new food processing technologies?," Agribusiness, John Wiley & Sons, Ltd., vol. 38(1), pages 73-92, January.
    16. Evelyne Gbénou-Sissinto & Ygué P. Adegbola & Gauthier Biaou & Roch C. Zossou, 2018. "Farmers’ Willingness to Pay for New Storage Technologies for Maize in Northern and Central Benin," Sustainability, MDPI, vol. 10(8), pages 1-21, August.
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    19. Jihee Lee & HyungBin Moon & Jongsu Lee, 2021. "Consumers’ heterogeneous preferences toward the renewable portfolio standard policy: An evaluation of Korea’s energy transition policy," Energy & Environment, , vol. 32(4), pages 648-667, June.
    20. Marianne Lefebvre & Pauline Laille & Masha Maslianskaia-Pautrel, 2020. "Individual preferences regarding pesticide-free management of green-spaces: a discret choice experiment with French citizens," Working Papers 2020.02, FAERE - French Association of Environmental and Resource Economists.
    21. Uddin, Azhar & Gallardo, R. Karina, 2021. "Consumers' willingness to pay for organic, clean label, and processed with a new food technology: an application to ready meals," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(3), March.
    22. Kairies-Schwarz, Nadja & Kokot, Johanna & Vomhof, Markus & Wessling, Jens, 2014. "How Do Consumers Choose Health Insurance? – An Experiment on Heterogeneity in Attribute Tastes and Risk Preferences," Ruhr Economic Papers 537, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    23. Saint-Cyr, Legrand D. F., 2016. "Accounting for farm heterogeneity in the assessment of agricultural policy impacts on structural change," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235778, Agricultural and Applied Economics Association.
    24. Nadja Kairies-Schwarz & Johanna Kokot & Markus Vomhof & Jens Wessling, 2014. "How Do Consumers Choose Health Insurance? – An Experiment on Heterogeneity in Attribute Tastes and Risk Preferences," Ruhr Economic Papers 0537, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.

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    More about this item

    Keywords

    Keywords: st0001; lclogit; latent class model; EM algorithm; mixed logit;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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