Докладчик: Sofya Budanova (Northwestern University)
Тема доклада: "Penalized Maximum Likelihood Estimation of Finite Mixture Models with Unknown Number of Components"
Место проведения: ул. Шаболовка д. 26, корп. 3, аудитори 3211
Тезисы доклада: Economic models often resort to finite mixtures to accommodate unobserved heterogeneity. In practice, the number of components in the mixture is rarely known. If too many components are included in the estimation, then the parameters of the estimated model are not pointidentified and lie on the boundary of the parameter space. This invalidates the classic results on maximum likelihood estimation. Nonetheless, the parsimonious model, which corresponds to a particular subset of the identified set, can be point-identified. I propose a method to estimate finite mixture models with an unknown number of components by maximizing a penalized likelihood function, where the penalty is applied to the mixing coefficients. The resulting Order-Selection-Consistent Estimator (OSCE) consistently estimates the true number of components in the mixture, and achieves the oracle efficiency for the parameters of the parsimonious model. This paper extends the literature on penalized estimation to the case of non-identified model parameters. Further, numerical simulations illustrate the performance of the proposed method in practice. Finally, the method is applied to the experimental data from Cornand and Heinemann  to determine the composition of subjects’ types associated with their level of rationality in a coordination game.
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