Instrumental variable estimation with heteroskedasticity and many instruments
Jerry Hausman,
Whitney Newey,
Tiemen Woutersen,
John Chao (chao@econ.umd.edu) and
Norman Swanson (nswanson@econ.rutgers.edu)
No CWP22/07, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies
Abstract:
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.
JEL-codes: C12 C13 C14 (search for similar items in EconPapers)
Date: 2007-09-01
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (47)
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Related works:
Journal Article: Instrumental variable estimation with heteroskedasticity and many instruments (2012)
Working Paper: Instrumental Variable Estimation with Heteroskedasticity and Many Instruments (2011)
Working Paper: Instrumental Variable Estimation with Heteroskedasticity and Many Instruments (2009)
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