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We consider estimation of and inference about coefficients on endogenous variables in a linear instrumental variables model where the number of instruments and exogenous control variables are each allowed to be larger than the sample size. We work within an approximately sparse framework that maintains that the signal available in the instruments and control variables may be effectively captured by a small number of the available variables. We provide a LASSO-based method for this setting which provides uniformly valid inference about the coefficients on endogenous variables. We illustrate the method through an application to demand estimation.

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Hansen, Christian -  Spindler, Martin -  Chernozhukov, Victor V - 

Id.: 69926447

Idioma: inglés (Estados Unidos)  - 

Versión: 1.0

Estado: Final

Tipo de recurso: Article  -  http://purl.org/eprint/type/JournalArticle  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

: Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.

Requerimientos técnicos:  Browser: Any - 

Relación: [IsBasedOn] American Economic Association
[References] http://dx.doi.org/10.1257/aer.p20151022
[References] American Economic Review

Fecha de contribución: 29-ago-2017


* 0002-8282
* Chernozhukov, Victor, Christian Hansen, and Martin Spindler. “ Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments .” American Economic Review 105, no. 5 (May 2015): 486–490. © 2017 American Economic Association.

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