Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/130025
Authors: 
Hu, Yingyao
Year of Publication: 
2015
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP03/15
Abstract: 
This paper reviews recent developments in nonparametric identi.cation of mea- surement 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 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 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.
Subjects: 
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 effect
panel data model
cognitive and noncognitive skills
matching
income dynamics
JEL: 
C01
C14
C22
C23
C26
C32
C33
C36
C57
C70
C78
D20
D31
D44
D83
D90
E24
I20
J21
J24
J60
L10
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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