High-dimensional instrumental variables regression and confidence sets

Eric Gautier and Christiern Rose
Authors registered in the RePEc Author Service: Alexandre B. Tsybakov

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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.

Keywords: Bias correction; Robustness to identification; Partial identification; Unknown variance; Variable selection; Instrumental variables; Sparsity; Endogeneity; Confidence intervals (search for similar items in EconPapers)
Date: 2021-08-03
New Economics Papers: this item is included in nep-ecm and nep-ore
Note: View the original document on HAL open archive server: https://hal.science/hal-00591732v7
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Citations: View citations in EconPapers (16)

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Related works:
Working Paper: High-dimensional instrumental variables regression and confidence sets (2019)
Working Paper: High-Dimensional Instrumental Variables Regression and Confidence Sets (2011)
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