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Partial Identification Review

Partial Identification Review - Review in Advance first...

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Partial Identification in Econometrics Elie Tamer Department of Economics, Northwestern University, Evanston, Illinois 60208; email: [email protected] Annu. Rev. Econ. 2010. 2:167–95 The Annual Review of Economics is online at econ.annualreviews.org This article’s doi: 10.1146/annurev.economics.050708.143401 Copyright © 2010 by Annual Reviews. All rights reserved 1941-1383/10/0904-0167$20.00 Key Words non-point-identified models, sensitivity analysis, robust inference, bounds Abstract Identification in econometric models maps prior assumptions and the data to information about a parameter of interest. The partial identification approach to inference recognizes that this process should not result in a binary answer that consists of whether the parameter is point identified. Rather, given the data, the partial identification approach characterizes the informational content of various assumptions by providing a menu of estimates, each based on different sets of assumptions, some of which are plausible and some of which are not. Of course, more assumptions beget more information, so stronger conclusions can be made at the expense of more assumptions. The partial identification approach advocates a more fluid view of identification and hence provides the empirical researcher with methods to help study the spectrum of information that we can harness about a parameter of interest using a menu of assumptions. This approach links conclusions drawn from various empirical models to sets of assumptions made in a transparent way. It allows researchers to examine the informational content of their assumptions and their impacts on the inferences made. Naturally, with finite sample sizes, this approach leads to statistical complica- tions, as one needs to deal with characterizing sampling uncertainty in models that do not point identify a parameter. Therefore, new methods for inference are developed. These methods construct con- fidence sets for partially identified parameters, and confidence regions for sets of parameters, or identifiable sets. 167 Review in Advance first posted online on February 25 , 20 10 . ( C hanges may still occur before final publication online and in print.) Changes may still occur before final publication online and in print. Annu. Rev. Econ. 2010.2. Downloaded from arjournals.annualreviews.org by NORTHWESTERN UNIVERSITY - Evanston Campus on 06/18/10. For personal use only.
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The law of decreasing credibility : The credibility of inference decreases with the strength of the assumptions maintained.” Manski (2003) “A fragile inference is not worth taking seriously.” Leamer (1985) 1. INTRODUCTION Partial identification in econometrics is an approach to conducting inference on parameters in econometric models that recognizes that identification is not an all-or-nothing concept and that models that do not point identify parameters of interest can, and typically do, contain valuable information about these parameters. This partial identification approach favors the principle that inference—and conclusions and actions—based on empirical models with fewer suspect assumptions is more robust, hence more sensible and believable.
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