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IV Regressions without Exclusion Restrictions

Wayne Yuan Gao, Rui Wang

arXiv 2 Apr 2023 · Econometrics · 3 citations (OpenAlex)

arXiv:2304.00626 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We study identification and estimation of endogenous linear and nonlinear regression models without excluded instrumental variables, based on the standard mean independence condition and a nonlinear relevance condition. Based on the identification results, we propose two semiparametric estimators as well as a discretization-based estimator that does not require any nonparametric regressions. We establish their asymptotic normality and demonstrate via simulations their robust finite-sample performances with respect to exclusion restrictions violations and endogeneity. Our approach is applied to study the returns to education, and to test the direct effects of college proximity indicators as well as family background variables on the outcome.

Citation extraction

54
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74
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Card, D (1993) Using geographic variation in college proximity to estimate the return to schooling0.87472100%
2Chernozhukov, V. and C. Hansen (2005) An IV model of quantile treatment effects0.73732100%
3Andrews, D. W. and X. Shi (2013) Inference based on conditional moment inequalities0.64422100%
4Chamberlain, G (1987) Asymptotic Efficiency in Estimation with Conditional Moment Restriction0.64422100%
5Imbens, G. W. and J. D. Angrist (1994) Identification and estimation of local average treatment effects0.64422100%
6Newey, W. K (1990) Efficient instrumental variables estimation of nonlinear models0.64422100%
7Robinson, P. M (1988) Root-N-consistent semiparametric regression0.58531100%
8Newey, K. and D. McFadden (1994) Large sample estimation and hypothesis testing0.5113233%
9Ambrosetti, A. and G. Prodi (1995) A primer of nonlinear analysis0.5112250%
10Escanciano, J. C (2018) A simple and robust estimator for linear regression models with strictly exogenous instruments0.51121100%

Showing the top 10 of 56 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Constructing an Instrument as a Function of Covariates0.73732
2A Distance Covariance-based Estimator0.64422
31420 Identification with possibly invalid IVs0.64422
42cmEndogeneity Corrections in Binary Outcome Models with Nonlinear Transformations: Identification and Inference0.40511
51820 Don't (fully) exclude me, it's not necessary! Causal inference with semi-IVs0.00011