A regularization approach to the many instruments problem

Carrasco, Marine

doi:10.1016/j.jeconom.2012.05.012

Cited by 114 publications

(154 citation statements)

References 47 publications

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“…Estimators studied here are the regularized 2SLS and LIML estimators introduced in Carrasco (2012) and Carrasco and Tchuente (2013). They can be used with many or even a continuum of instruments.…”

Section: Presentation Of the Regularized 2sls And Liml Estimatorsmentioning

confidence: 99%

“…The extension of the generalized method of moments described in Carrasco and Florens (2000), Carrasco andFlorens (2012), andCarrasco (2012) gives the regularized 2SLS.…”

Section: Presentation Of the Regularized 2sls And Liml Estimatorsmentioning

confidence: 99%

“…In Carrasco (2012) and Carrasco and Tchuente (2013), modi…ed IV estimators based on di¤erent regularization schemes were proposed. These estimators improved the small sample properties of IV estimators when the number of instruments is very large or in…nite.…”

Section: Asymptotic Propertiesmentioning

confidence: 99%

“…In the simulations, we use a data-driven method for selecting based on an expansion of the MSE and proposed in Carrasco (2012) and Carrasco and Tchuente (2013). These selection criteria were derived assuming strong instruments and may not be valid in presence of weak instruments.…”

Section: Related Literaturementioning

confidence: 99%

“…All these methods involve a regularization parameter, which is the counterpart of the smoothing parameter that appears in the nonparametric literature. A data driven method was developed in Carrasco (2012) and Carrasco and Tchuente (2013) to select the best regularization parameter when the instruments are strong. We use these methods in our simulations for selecting the regularization parameter when the instruments are weak but we do not prove that these methods are valid in this case.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Estimation with Many Weak Instruments Using Regularization Techniques

Carrasco

Tchuente

2013

SSRN Journal

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Section: Presentation Of the Regularized 2sls And Liml Estimatorsmentioning

confidence: 99%

“…The extension of the generalized method of moments described in Carrasco and Florens (2000), Carrasco andFlorens (2012), andCarrasco (2012) gives the regularized 2SLS.…”

Section: Presentation Of the Regularized 2sls And Liml Estimatorsmentioning

confidence: 99%

Section: Asymptotic Propertiesmentioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Estimation with Many Weak Instruments Using Regularization Techniques

Carrasco

Tchuente

2013

SSRN Journal

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Dummy endogenous treatment effect estimation using high‐dimensional instrumental variables

et al. 2021

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We develop a two‐stage approach to estimate the treatment effects of dummy endogenous variables using high‐dimensional instrumental variables (IVs). In the first stage, instead of using a conventional linear reduced‐form regression to approximate the optimal instrument, we propose a penalized logistic reduced‐form model to accommodate both the binary nature of the endogenous treatment variable and the high dimensionality of the IVs. In the second stage, we replace the original treatment variable with its estimated propensity score and run a least‐squares regression to obtain a penalized logistic regression instrumental variables estimator (LIVE). We show theoretically that the proposed LIVE is root‐n consistent with the true treatment effect and asymptotically normal. Monte Carlo simulations demonstrate that LIVE is more efficient than existing IV estimators for endogenous treatment effects. In applications, we use LIVE to investigate whether the Olympic Games facilitate the host nation's economic growth and whether home visits from teachers enhance students' academic performance. In addition, the R functions for the proposed algorithms have been developed in an R package naivereg.

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Instrumental variable model average with applications in Mendelian randomization

Seng

Liu

Wang

et al. 2023

Statistics in Medicine

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Mendelian randomization is a technique used to examine the causal effect of a modifiable exposure on a trait using an observational study by utilizing genetic variants. The use of many instruments can help to improve the estimation precision but may suffer bias when the instruments are weakly associated with the exposure. To overcome the difficulty of high‐dimensionality, we propose a model average estimator which involves using different subsets of instruments (single nucleotide polymorphisms, SNPs) to predict the exposure in the first stage, followed by weighting the submodels' predictions using penalization by common penalty functions such as least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). The model averaged predictions are then used as a genetically predicted exposure to obtain the estimation of the causal effect on the response in the second stage. The novelty of our model average estimator also lies in that it allows the number of submodels and the submodels' sizes to grow with the sample size. The practical performance of the estimator is examined in a series of numerical studies. We apply the proposed method on a real genetic dataset investigating the relationship between stature and blood pressure.

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A regularization approach to the many instruments problem

Cited by 114 publications

References 47 publications

Efficient Estimation with Many Weak Instruments Using Regularization Techniques

Efficient Estimation with Many Weak Instruments Using Regularization Techniques

Dummy endogenous treatment effect estimation using high‐dimensional instrumental variables

Instrumental variable model average with applications in Mendelian randomization

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