Partial Identification in Econometrics
AbstractIdentification 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 complications, 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 confidence sets for partially identified parameters, and confidence regions for sets of parameters, or identifiable sets.
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Bibliographic InfoArticle provided by Annual Reviews in its journal Annual Review of Economics.
Volume (Year): 2 (2010)
Issue (Month): 1 (09)
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- C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
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- Arie Beresteanu & Ilya Molchanov & Francesca Molinari, 2010.
"Partial identification using random set theory,"
CeMMAP working papers
CWP40/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Liao, Yuan & Simoni, Anna, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," MPRA Paper 43262, University Library of Munich, Germany.
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