Instrumental Variable Estimation with Heteroskedasticity and Many Instruments

By Jerry A. Hausman, Whitney K. Newey, Tiemen Woutersen, John C. Chao, Norman R. Swanson. 2012

165 citations recorded by the source corpus.

It is common practice in econometrics to correct for heteroskedasticity. This paper corrects instrumental variables estimators with many instruments for heteroskedasticity. We give heteroskedasticity robust versions of the limited information maximum likelihood (LIML) and Fuller (1977, FULL) estimators; as well as heteroskedasticity consistent standard errors thereof. The estimators are based on removing the own observation terms in the numerator of the LIML variance ratio. We derive asymptotic properties of the estimators under many and many weak instruments setups. Based on a series of Monte Carlo experiments, we find that the estimators perform as well as LIML or FULL under homoskedasticity, and have much lower bias and dispersion under heteroskedasticity, in nearly all cases considered.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/8b92f95c6d7b1e7724badd51c851e1a34af36764

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In Economics and Econometrics

Citations in the corpus

Massachusetts Institute of Technology Department of Economics Working Paper Series Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain (unknown): “…in efficiency is appealing, but recent work in econometrics has also demonstrated that IV estimators that make use of many instruments may have very poor properties ; see, for example, [3], [18], [27], and [28] which propose solutions for this problem based on " many-instrument " asymptotics.”
Bartik Instruments: What, When, Why, and How (2020, unknown): “In particular, we use the HFUL estimator from Hausman et al. (2012), which is a heteroskedasticity robust version of the Fuller (1977) estimator (hence, H-FUL). This estimator is a jackknife estimator. See Hausman et al. (2012) for further discussion 13In Appendix E we write down an economic model w”
ESTIMATION IN AN INSTRUMENTAL VARIABLES MODEL WITH TREATMENT EFFECT HETEROGENEITY (2012, background): “• If there are no covariates Wi, then the hlim estimator of Hausman et al. (2012) also admits this minimum distance representation, with Ξ̂ = 1 n ( Y T )′ (HZ −DZ) ( Y T ) , Ŝ = 1 n− K ( Y T )′ (MZ + DZ) ( Y T ) .”
The effect of disability insurance receipt on labor supply (2014, background): “Comparing equations (12)-(14) shows that the individual’s optimal decision rule is”