Optimal Decision Rules for Weak GMM
<jats:p>This paper studies optimal decision rules, including estimators and tests, for weakly identified GMM models. We derive the limit experiment for weakly identified GMM, and propose a theoretically‐motivated class of priors which give rise to quasi‐Bayes decision rules as a limiting case....
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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The Econometric Society
2022
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| Online Access: | https://hdl.handle.net/1721.1/145192 |
| _version_ | 1811094491793195008 |
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| author | Andrews, Isaiah Mikusheva, Anna |
| author2 | Massachusetts Institute of Technology. Department of Economics |
| author_facet | Massachusetts Institute of Technology. Department of Economics Andrews, Isaiah Mikusheva, Anna |
| author_sort | Andrews, Isaiah |
| collection | MIT |
| description | <jats:p>This paper studies optimal decision rules, including estimators and tests, for weakly identified GMM models. We derive the limit experiment for weakly identified GMM, and propose a theoretically‐motivated class of priors which give rise to quasi‐Bayes decision rules as a limiting case. Together with results in the previous literature, this establishes desirable properties for the quasi‐Bayes approach regardless of model identification status, and we recommend quasi‐Bayes for settings where identification is a concern. We further propose weighted average power‐optimal identification‐robust frequentist tests and confidence sets, and prove a Bernstein‐von Mises‐type result for the quasi‐Bayes posterior under weak identification.</jats:p> |
| first_indexed | 2024-09-23T16:01:09Z |
| format | Article |
| id | mit-1721.1/145192 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:01:09Z |
| publishDate | 2022 |
| publisher | The Econometric Society |
| record_format | dspace |
| spelling | mit-1721.1/1451922023-03-24T20:26:37Z Optimal Decision Rules for Weak GMM Andrews, Isaiah Mikusheva, Anna Massachusetts Institute of Technology. Department of Economics <jats:p>This paper studies optimal decision rules, including estimators and tests, for weakly identified GMM models. We derive the limit experiment for weakly identified GMM, and propose a theoretically‐motivated class of priors which give rise to quasi‐Bayes decision rules as a limiting case. Together with results in the previous literature, this establishes desirable properties for the quasi‐Bayes approach regardless of model identification status, and we recommend quasi‐Bayes for settings where identification is a concern. We further propose weighted average power‐optimal identification‐robust frequentist tests and confidence sets, and prove a Bernstein‐von Mises‐type result for the quasi‐Bayes posterior under weak identification.</jats:p> 2022-08-29T17:54:44Z 2022-08-29T17:54:44Z 2022 2022-08-29T17:25:04Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/145192 Andrews, Isaiah and Mikusheva, Anna. 2022. "Optimal Decision Rules for Weak GMM." Econometrica, 90 (2). en 10.3982/ECTA18678 Econometrica Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf The Econometric Society arXiv |
| spellingShingle | Andrews, Isaiah Mikusheva, Anna Optimal Decision Rules for Weak GMM |
| title | Optimal Decision Rules for Weak GMM |
| title_full | Optimal Decision Rules for Weak GMM |
| title_fullStr | Optimal Decision Rules for Weak GMM |
| title_full_unstemmed | Optimal Decision Rules for Weak GMM |
| title_short | Optimal Decision Rules for Weak GMM |
| title_sort | optimal decision rules for weak gmm |
| url | https://hdl.handle.net/1721.1/145192 |
| work_keys_str_mv | AT andrewsisaiah optimaldecisionrulesforweakgmm AT mikushevaanna optimaldecisionrulesforweakgmm |