Abstract
The problem of identification is defined in terms of the possibility of characterizing parameters of interest from observable data. This problem occurs in many fields, such as automatic control, biomedical engineering, psychology, systems science, the design of experiments, and econometrics. This article focuses on identification in econometric models, which typically involve random variables. Identification in general parametric statistical models is defined, and its meaning in a number of specific econometric models is considered: regression (collinearity), simultaneous equations, dynamic models, and nonlinear models. Identification in nonparametric models, weak identification, and the statistical implications of identification failure are also discussed.
Keywords
- Bayes’ th
- Collinearity
- Endogeneity and exogeneity
- Identification
- Instrumental variable
- Linear models
- Multivariate regression models
- Nonparametric estimation
- Nonparametric models
- Probability
- Random variables
- Returns to schooling
- Separability
- Serial correlation
- Simultaneous equations models
- Treatment effect
- Weak identification
- Weak instruments
JEL Classifications
This chapter was originally published in The New Palgrave Dictionary of Economics, 2nd edition, 2008. Edited by Steven N. Durlauf and Lawrence E. Blume
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Dufour, JM., Hsiao, C. (2008). Identification. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95121-5_1000-2
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DOI: https://doi.org/10.1057/978-1-349-95121-5_1000-2
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Identification- Published:
- 18 April 2017
DOI: https://doi.org/10.1057/978-1-349-95121-5_1000-2
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- 02 November 2016
DOI: https://doi.org/10.1057/978-1-349-95121-5_1000-1