Microeconomic Models with Latent Variables: Applications of Measurement Error Models in Empirical Industrial Organization and Labor Economics

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThis paper reviews recent developments in nonparametric identi…cation 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 identi…cation 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 may be suitable for many microeconomic models with latent variables, such as models with unobserved heterogeneity or unobserved state variables and panel data models with …xed e¤ects. Recent developments in measurement error models allow very ‡exible speci…cation 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.JEL classi…cation:

Unobserved heterogeneity in auctions under restricted stochastic dominance

“…While we focus on auction models in this paper, the RSD ordering can be useful in other applications of measurement error models. See Hu (2015) for a recent survey on applications in empirical industrial organization and labor economics. Moreover, although we study only discrete unobserved heterogeneity in this paper, the RSD ordering can also be applied in the continuous case.…”

“…Applying the results in Hu (2008), Hu, McAdams, and Shum (2013) obtain their key identification equation:…”

“…where Applying the results in Hu (2008), he identifies the type specific bid distribution G k (•) and then the value distribution F k (•) under Assumption 3. In view of our results, his results hold under a weaker condition Assumption 4.…”

“…The second one is the misclassification approach of Hu, McAdams, and Shum (2013). They consider discrete one-dimensional unobserved heterogeneity and achieve identification with at least three bidders per auction relying on the results of Hu (2008).…”

Nonlinear panel data estimation via quantile regressions

“…The class of panel data models introduced above satisfies conditional independence restrictions, as period‐specific outcomes are mutually independent conditional on exogenous covariates and individual heterogeneity . A body of work, initially developed in the context of nonlinear measurement error models, has established nonparametric identification results in related models under conditional independence restrictions; see Hu () for a recent survey. Here we show how the result in Hu and Schennach () can be used to show nonparametric identification.…”