arXiv 7 Nov 2018 · Econometrics · 3 citations (OpenAlex)
arXiv:1811.02727 · PDF · DOI · OpenAlex · Extracted main text
Finite mixture models are useful in applied econometrics. They can be used to model unobserved heterogeneity, which plays major roles in labor economics, industrial organization and other fields. Mixtures are also convenient in dealing with contaminated sampling models and models with multiple equilibria. This paper shows that finite mixture models are nonparametrically identified under weak assumptions that are plausible in economic applications. The key is to utilize the identification power implied by information in covariates variation. First, three identification approaches are presented, under distinct and non-nested sets of sufficient conditions. Observable features of data inform us which of the three approaches is valid. These results apply to general nonparametric switching regressions, as well as to structural econometric models, such as auction models with unobserved heterogeneity. Second, some extensions of the identification results are developed. In particular, a mixture regression where the mixing weights depend on the value of the regressors in a fully unrestricted manner is shown to be nonparametrically identifiable. This means a finite mixture model with function-valued unobserved heterogeneity can be identified in a cross-section setting, without restricting the dependence pattern between the regressor and the unobserved heterogeneity. In this aspect it is akin to fixed effects panel data models which permit unrestricted correlation between unobserved heterogeneity and covariates. Third, the paper shows that fully nonparametric estimation of the entire mixture model is possible, by forming a sample analogue of one of the new identification strategies. The estimator is shown to possess a desirable polynomial rate of convergence as in a standard nonparametric estimation problem, despite nonregular features of the model.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Teicher (1963) Identifiability of finite mixtures | 0.644 | 2 | 2 | 100% |
| 2 | Einmahl and Mason (2005) Uniform in bandwidth consistency of kernel-type function estimators | 0.585 | 3 | 1 | 100% |
| 3 | Haile and Kitamura (2018) Unobserved Heterogeneity in Auctions | 0.585 | 3 | 1 | 100% |
| 4 | Haile, Hong, and Shum (2003) Nonparametric tests for common values at first-price sealed-bid auctions | 0.511 | 2 | 1 | 100% |
| 5 | Heckman and Singer (1984) A method for minimizing the impact of distributional assumptions in econometric models for duration data | 0.511 | 2 | 1 | 100% |
| 6 | Compiani, Haile, and Sant'Anna (2018) Common Values, Unobserverd Heterogeneity, and Endogenous Entry in U.S. Offshore Oil Lease Auctions | 0.405 | 1 | 1 | 100% |
| 7 | Adams (2016) Finite mixture models with one exclusion restriction | 0.405 | 1 | 1 | 100% |
| 8 | Aguirregabiria and Mira (2013) Identification of games of incomplete information with multiple equilibria and common unobserved heterogeneity | 0.405 | 1 | 1 | 100% |
| 9 | Arcidiacono and Miller (2011) Conditional choice probability estimation of dynamic discrete choice models with unobserved heterogeneity | 0.405 | 1 | 1 | 100% |
| 10 | Athey and Haile (2007) Nonparametric approaches to auctions | 0.405 | 1 | 1 | 100% |
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