A Geometric Approach to Nonlinear Econometric Models
Conventional tests for composite hypotheses in minimum distance models can be unreliable when the relationship between the structural and reduced‐form parameters is highly nonlinear. Such nonlinearity may arise for a variety of reasons, including weak identification. In this note, we begin by studyi...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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The Econometric Society
2017
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| Online Access: | http://hdl.handle.net/1721.1/106933 https://orcid.org/0000-0002-0724-5428 |
| _version_ | 1826201139188596736 |
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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 | Conventional tests for composite hypotheses in minimum distance models can be unreliable when the relationship between the structural and reduced‐form parameters is highly nonlinear. Such nonlinearity may arise for a variety of reasons, including weak identification. In this note, we begin by studying the problem of testing a “curved null” in a finite‐sample Gaussian model. Using the curvature of the model, we develop new finite‐sample bounds on the distribution of minimum‐distance statistics. These bounds allow us to construct tests for composite hypotheses which are uniformly asymptotically valid over a large class of data generating processes and structural models. |
| first_indexed | 2024-09-23T11:46:50Z |
| format | Article |
| id | mit-1721.1/106933 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:46:50Z |
| publishDate | 2017 |
| publisher | The Econometric Society |
| record_format | dspace |
| spelling | mit-1721.1/1069332022-10-01T06:00:01Z A Geometric Approach to Nonlinear Econometric Models Andrews, Isaiah Mikusheva, Anna Massachusetts Institute of Technology. Department of Economics Mikusheva, Anna Mikusheva, Anna Conventional tests for composite hypotheses in minimum distance models can be unreliable when the relationship between the structural and reduced‐form parameters is highly nonlinear. Such nonlinearity may arise for a variety of reasons, including weak identification. In this note, we begin by studying the problem of testing a “curved null” in a finite‐sample Gaussian model. Using the curvature of the model, we develop new finite‐sample bounds on the distribution of minimum‐distance statistics. These bounds allow us to construct tests for composite hypotheses which are uniformly asymptotically valid over a large class of data generating processes and structural models. Massachusetts Institute of Technology (Castle-Krob Career Development Chair) Alfred P. Sloan Foundation (Sloan Research Fellowship) 2017-02-15T14:48:42Z 2017-02-15T14:48:42Z 2016-05 Article http://purl.org/eprint/type/JournalArticle 0012-9682 1468-0262 http://hdl.handle.net/1721.1/106933 Andrews, Isaiah, and Anna Mikusheva. “A Geometric Approach to Nonlinear Econometric Models.” Econometrica 84.3 (2016): 1249–1264. https://orcid.org/0000-0002-0724-5428 en_US http://dx.doi.org/10.3982/ECTA12030 Econometrica Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf The Econometric Society Mikusheva |
| spellingShingle | Andrews, Isaiah Mikusheva, Anna A Geometric Approach to Nonlinear Econometric Models |
| title | A Geometric Approach to Nonlinear Econometric Models |
| title_full | A Geometric Approach to Nonlinear Econometric Models |
| title_fullStr | A Geometric Approach to Nonlinear Econometric Models |
| title_full_unstemmed | A Geometric Approach to Nonlinear Econometric Models |
| title_short | A Geometric Approach to Nonlinear Econometric Models |
| title_sort | geometric approach to nonlinear econometric models |
| url | http://hdl.handle.net/1721.1/106933 https://orcid.org/0000-0002-0724-5428 |
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