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Simple Two-Stage Inference for A Class of Partially Identified Models

Shi, Xiaoxia and Shum, Matthew (2013) Simple Two-Stage Inference for A Class of Partially Identified Models. Social Science Working Paper, 1376. California Institute of Technology , Pasadena, CA. (Unpublished)

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This note proposes a new two-stage estimation and inference procedure for a class of partially identified models. The procedure can be considered an extension of classical minimum distance estimation procedures to accommodate inequality constraints and partial identification. It involves no tuning parameter, is nonconservative and is conceptually and computationally simple. The class of models includes models of interest to applied researchers, including the static entry game, a voting game with communication and a discrete mixture model.

Item Type:Report or Paper (Working Paper)
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URLURL TypeDescription ItemUpdated version published as journal article
Shum, Matthew0000-0002-6262-915X
Additional Information:May 2013. We thank Yanqin Fan, Patrik Guggenberger, Bruce Hansen and Jack Porter for useful comments and suggestions.
Group:Social Science Working Papers
Subject Keywords:Implicit Function Theorem, Hausdorff Consistency, Minimum Distance, Partial Identification, Two-stage Inference
Series Name:Social Science Working Paper
Issue or Number:1376
Record Number:CaltechAUTHORS:20160330-152952577
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65778
Deposited By: Katherine Johnson
Deposited On:30 Mar 2016 22:39
Last Modified:03 Oct 2019 09:50

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