The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor economics
Journal of Econometrics, 2017, vol. 200, issue 2, 154-168
This paper reviews the recent developments in nonparametric identification of measurement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics. Measurement error models describe mappings from a latent distribution to an observed distribution. The identification and estimation of measurement error models focus on how to obtain the latent distribution and the measurement error distribution from the observed distribution. Such a framework is suitable for many microeconomic models with latent variables, such as models with unobserved heterogeneity or unobserved state variables and panel data models with fixed effects. Recent developments in measurement error models allow very flexible specification of the latent distribution and the measurement error distribution. These developments greatly broaden economic applications of measurement error models. This paper provides an accessible introduction of these technical results to empirical researchers so as to expand applications of measurement error models.
Keywords: Measurement error model; Errors-in-variables; Latent variable; Unobserved heterogeneity; Unobserved state variable; Mixture model; Hidden Markov model; Dynamic discrete choice; Nonparametric identification; Conditional independence; Endogeneity; Instrument; Type; Unemployment rates; IPV auction; Multiple equilibria; Incomplete information game; Belief; Learning model; Fixed effects; Panel data model; Cognitive and non-cognitive skills; Matching; Income dynamics (search for similar items in EconPapers)
JEL-codes: C01 C14 C22 C23 C26 C32 C33 C36 C57 C70 C78 D20 D31 D44 D83 D90 E24 I20 J21 J24 J60 L10 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:200:y:2017:i:2:p:154-168
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