Inference for Large‐Scale Linear Systems With Known Coefficients.
This paper considers the problem of testing whether there exists a non‐negative solution to a possibly under‐determined system of linear equations with known coefficients. This hypothesis testing problem arises naturally in a number of settings, including random coefficient, treatment effect, and di...
| Publicado en: | Econometrica Vol. 91; no. 1; pp. 299 - 328 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Wiley-Blackwell
Jan2023
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=161618313&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 161618313 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: Y pubinfo: dt: Jan2023 vid: 91 iid: 1 pid: 480 pub: Wiley-Blackwell artinfo: ui: 161618313 10.3982/ECTA18979 ppf: 299 ppct: 29 formats: tig: atl: Inference for Large‐Scale Linear Systems With Known Coefficients. aug: au: Fang, Zheng Santos, Andres Shaikh, Azeem M. Torgovitsky, Alexander affil: Department of Economics, Emory University Department of Economics, UCLA Department of Economics, University of Chicago su: Reality therapy Linear systems Discrete choice models Linear programming Linear equations Null hypothesis sug: subj: Reality therapy Linear systems Discrete choice models Linear programming Linear equations Null hypothesis keyword: exchangeable bootstrap linear inequalities moment inequalities partial identification random coefficients uniform inference exchangeable bootstrap linear inequalities moment inequalities partial identification random coefficients uniform inference ab: This paper considers the problem of testing whether there exists a non‐negative solution to a possibly under‐determined system of linear equations with known coefficients. This hypothesis testing problem arises naturally in a number of settings, including random coefficient, treatment effect, and discrete choice models, as well as a class of linear programming problems. As a first contribution, we obtain a novel geometric characterization of the null hypothesis in terms of identified parameters satisfying an infinite set of inequality restrictions. Using this characterization, we devise a test that requires solving only linear programs for its implementation, and thus remains computationally feasible in the high‐dimensional applications that motivate our analysis. The asymptotic size of the proposed test is shown to equal at most the nominal level uniformly over a large class of distributions that permits the number of linear equations to grow with the sample size. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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