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High-dimensional instrumental variables regression and confidence sets

Author

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  • Eric Gautier

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Christiern Rose

    (UQ [All campuses : Brisbane, Dutton Park Gatton, Herston, St Lucia and other locations] - The University of Queensland)

Abstract

This article considers inference in linear instrumental variables models with many regressors, all of which could be endogenous. We propose the STIV estimator. Identification robust confidence sets are derived by solving linear programs. We present results on rates of convergence, variable selection, confidence sets which adapt to the sparsity, and analyze confidence bands for vectors of linear functions using bias correction. We also provide solutions to some instruments being endogenous. The application is to the EASI demand system.

Suggested Citation

  • Eric Gautier & Christiern Rose, 2021. "High-dimensional instrumental variables regression and confidence sets," Working Papers hal-00591732, HAL.
    • Handle: RePEc:hal:wpaper:hal-00591732
    Note: View the original document on HAL open archive server: https://hal.science/hal-00591732v7
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    References listed on IDEAS

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