Replication data for: Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments

Version
2
Resource Type
Dataset
Creator
  • Chernozhukov, Victor
  • Hansen, Christian
  • Spindler, Martin
Publication Date
2021-02-23
Description
  • Abstract

    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.
Availability
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This study is freely available to the general public via web download.
Relations
  • Is version of
    DOI: 10.3886/E113367
Publications
  • 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): 486–90. https://doi.org/10.1257/aer.p20151022.
    • ID: 10.1257/aer.p20151022 (DOI)

Update Metadata: 2021-02-23 | Issue Number: 1 | Registration Date: 2021-02-23