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stratEst: Strategy Estimation in R

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  • Fabian Dvorak

Abstract

stratEst is a software package for the estimation of finite mixture models of discrete choice strategies in the statistical computing environment R. Discrete choice strategies can be customized by the user to fit the environment in which choices are made. The parameters of the strategy estimation model describe the behavior of each strategy and how frequent each strategy is in the population. The estimation function of the package uses the expectation maximization algorithm and the Newton-Raphson method to find the maximum likelihood estimates of the model parameters. The estimation function can also be used to fit a strategy estimation model with individual level covariates to explain the selection of strategies by individuals. The package contains functions for data processing and simulation, strategy generation, parameter tests, model checking, and model selection.

Suggested Citation

  • Fabian Dvorak, 2020. "stratEst: Strategy Estimation in R," TWI Research Paper Series 119, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    • Handle: RePEc:twi:respas:0119
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    References listed on IDEAS

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    1. Matthew Embrey & Guillaume R Fréchette & Sevgi Yuksel, 2018. "Cooperation in the Finitely Repeated Prisoner’s Dilemma," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 509-551.
    2. Masaki Aoyagi & V. Bhaskar & Guillaume R. Fréchette, 2019. "The Impact of Monitoring in Infinitely Repeated Games: Perfect, Public, and Private," American Economic Journal: Microeconomics, American Economic Association, vol. 11(1), pages 1-43, February.
    3. Guillaume R. Fréchette & Sevgi Yuksel, 2017. "Infinitely repeated games in the laboratory: four perspectives on discounting and random termination," Experimental Economics, Springer;Economic Science Association, vol. 20(2), pages 279-308, June.
    4. Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
    5. Arechar, Antonio A. & Dreber, Anna & Fudenberg, Drew & Rand, David G., 2017. "“I'm just a soul whose intentions are good”: The role of communication in noisy repeated games," Games and Economic Behavior, Elsevier, vol. 104(C), pages 726-743.
    6. Dvorak, Fabian & Fehrler, Sebastian, 2018. "Negotiating Cooperation under Uncertainty: Communication in Noisy, Indefinitely Repeated Interactions," IZA Discussion Papers 11897, Institute of Labor Economics (IZA).
    7. Bull, Shelley B. & Mak, Carmen & Greenwood, Celia M. T., 2002. "A modified score function estimator for multinomial logistic regression in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 39(1), pages 57-74, March.
    8. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    9. Yves Breitmoser, 2015. "Cooperation, but No Reciprocity: Individual Strategies in the Repeated Prisoner's Dilemma," American Economic Review, American Economic Association, vol. 105(9), pages 2882-2910, September.
    10. Bolck, Annabel & Croon, Marcel & Hagenaars, Jacques, 2004. "Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators," Political Analysis, Cambridge University Press, vol. 12(1), pages 3-27, January.
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    Keywords

    discrete choice strategies; finite mixture model; R;
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