CEBA talks 2020-2021
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Penalized estimation of finite mixture models

Authors: Sofya Budanova


Abstract: Economists often model unobserved heterogeneity using finite mixtures. In practice, the number of mixture components is rarely known. Model parameters lack point-identification if the estimation includes too many components, thus invalidating the classic properties of maximum likelihood estimation. I propose a penalized likelihood method to estimate finite mixtures with an unknown number of components. The resulting Order-Selection-Consistent Estimator (OSCE) consistently estimates the true number of components and achieves oracle efficiency. This paper extends penalized estimation to models without point-identification and to mixtures with growing number of components. I apply the OSCE to estimate players’ rationality level in a coordination game.


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