Published May 11, 2013
| Accepted Version
Working Paper
Open
Simple Two-Stage Inference for A Class of Partially Identified Models
- Creators
- Shi, Xiaoxia
-
Shum, Matthew
Chicago
Abstract
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.
Additional Information
May 2013. We thank Yanqin Fan, Patrik Guggenberger, Bruce Hansen and Jack Porter for useful comments and suggestions.Attached Files
Accepted Version - sswp1376.pdf
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sswp1376.pdf
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Additional details
- Eprint ID
- 65778
- Resolver ID
- CaltechAUTHORS:20160330-152952577
- Created
-
2016-03-30Created from EPrint's datestamp field
- Updated
-
2019-10-03Created from EPrint's last_modified field
- Caltech groups
- Social Science Working Papers
- Series Name
- Social Science Working Paper
- Series Volume or Issue Number
- 1376