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Partial Identification in Econometrics

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  • Elie Tamer

    ()
    (Department of Economics, Northwestern University, Evanston, Illinois 60208)

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    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 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 Info

    Article provided by Annual Reviews in its journal Annual Review of Economics.

    Volume (Year): 2 (2010)
    Issue (Month): 1 (09)
    Pages: 167-195

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    Handle: RePEc:anr:reveco:v:2:y:2010:p:167-195

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    Related research

    Keywords: non-point-identified models; sensitivity analysis; robust inference; bounds;

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    Cited by:
    1. Ian M. McCarthy & Daniel L. Millimet & Manan Roy, 2014. "Bounding Treatment Effects: Stata Command for the Partial Identification of the Average Treatment Effect with Endogenous and Misreported Treatment Assignment," Emory Economics 1407, Department of Economics, Emory University (Atlanta).
    2. Wooyoung Kim & Koohyun Kwon & Soonwoo Kwon & Sokbae 'Simon' Lee, 2014. "The identification power of smoothness assumptions in models with counterfactual outcomes," CeMMAP working papers CWP17/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. 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.
    4. Liao, Yuan & Simoni, Anna, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," MPRA Paper 43262, University Library of Munich, Germany.
    5. Larry G. Epstein & Kyoungwon Seo, 2013. "De Finetti Meets Ellsberg," CIRANO Working Papers 2013s-35, CIRANO.
    6. Nicolai V. Kuminoff & V. Kerry Smith & Christopher Timmins, 2013. "The New Economics of Equilibrium Sorting and Policy Evaluation Using Housing Markets," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1007-62, December.

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