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Ridge regularization for Mean Squared Error Reduction in Regression with Weak Instruments

Karthik Rajkumar

arXiv 18 Apr 2019 · Econometrics

arXiv:1904.08580 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In this paper, I show that classic two-stage least squares (2SLS) estimates are highly unstable with weak instruments. I propose a ridge estimator (ridge IV) and show that it is asymptotically normal even with weak instruments, whereas 2SLS is severely distorted and un-bounded. I motivate the ridge IV estimator as a convex optimization problem with a GMM objective function and an L2 penalty. I show that ridge IV leads to sizable mean squared error reductions theoretically and validate these results in a simulation study inspired by data designs of papers published in the American Economic Review.

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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
1Andrews, I., J. Stock, and L. Sun (2018) Weak instruments in iv regression: Theory and practice, Tech0.51121100%
2Young, A (2018) Consistency without inference: Instrumental variables in practical application0.51121100%
3Anderson, T. W., H. Rubin, et al (1949) Estimation of the parameters of a single equation in a complete system of stochastic equations0.40511100%
4Andrews, I. and T. B. Armstrong (2017) Unbiased instrumental variables estimation under known first-stage sign0.40511100%
5Hausman, J. A (1978) Specification tests in econometrics0.40511100%
6Hirano, K. and J. R. Porter (2015) Location properties of point estimators in linear instrumental variables and related models0.40511100%
7Hornung, E (2014) Immigration and the diffusion of technology: The Huguenot diaspora in Prussia0.40511100%
8Knight, K., W. Fu, et al (2000) Asymptotics for lasso-type estimators0.40511100%
9Olea, J. L. M. and C. Pflueger (2013) A robust test for weak instruments0.40511100%
10Staiger, D. and J. H. Stock (1997) Instrumental Variables Regression with Weak Instruments0.40511100%

Showing the top 10 of 10 scored citations.