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csa2sls: A complete subset approach for many instruments using Stata

Seojeong Lee, Siha Lee, Julius Owusu, Youngki Shin

arXiv 4 Jul 2022 · Econometrics · publishedThe Stata Journal Promoting communications on statistics and Stata (2023)

arXiv:2207.01533 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We develop a Stata command $csa2sls$ that implements the complete subset averaging two-stage least squares (CSA2SLS) estimator in Lee and Shin (2021). The CSA2SLS estimator is an alternative to the two-stage least squares estimator that remedies the bias issue caused by many correlated instruments. We conduct Monte Carlo simulations and confirm that the CSA2SLS estimator reduces both the mean squared error and the estimation bias substantially when instruments are correlated. We illustrate the usage of $csa2sls$ in Stata by an empirical application.

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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
1Lee, S., and Y. Shin (2021) Complete subset averaging with many instruments self1.00075100%
2Donald, S. G., and W. K. Newey (2001) Choosing the number of instruments0.51121100%
3Kuersteiner, G., and R. Okui (2010) Constructing optimal instruments by first-stage prediction averaging0.51121100%
4Berry, S., J. Levinsohn, and A. Pakes (1995) Automobile prices in market equilibrium0.40511100%
5Lee, S., Y. Liao, M. H. Seo, and Y. Shin (2022) Fast and robust online inference with stochastic gradient descent via random scaling self0.40511100%

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