Penalized estimation of finite mixture models

Sofya Budanova

Journal of Econometrics, 2025, vol. 249, issue PB

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 levels in a coordination game.

Keywords: Big data; LASSO; SCAD; Non-identification; Bounded rationality; Finite mixtures; Clustering (search for similar items in EconPapers)
JEL-codes: C13 C18 C52 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:249:y:2025:i:pb:s0304407625000120

DOI: 10.1016/j.jeconom.2025.105958

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