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Efficient Estimation Using Regularized Jackknife IV Estimator

Author

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  • Marine CARRASCO
  • Mohamed DOUKALI

Abstract

We consider instrumental variables (IV) regression in a setting with many (possibly weak) instruments. In finite samples, the inclusion of an excessive number of moments may increase the bias of IV estimators. We propose a Jackknife instrumental variables estimator (RJIVE) combined with regularization techniques based on Tikhonov (T), Principal Components (PC) and Landweber Fridman (LF) methods to stabilize the projection matrix. We prove that the RJIVE is consistent and asymptotically normally distributed. Moreover, it reaches the semi parametric efficiency bound under certain conditions. We derive the rate of the approximate mean square error and propose a data-driven method for selecting the tuning parameter. Simulation results show that our proposed estimators provide more reliable confidence intervals than other regularized estimators.

Suggested Citation

  • Marine CARRASCO & Mohamed DOUKALI, 2017. "Efficient Estimation Using Regularized Jackknife IV Estimator," Annals of Economics and Statistics, GENES, issue 128, pages 109-149.
    • Handle: RePEc:adr:anecst:y:2017:i:128:p:109-149
    DOI: 10.15609/annaeconstat2009.128.0109
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    Citations

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    Cited by:

    1. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2020. "Ill-posed estimation in high-dimensional models with instrumental variables," Journal of Econometrics, Elsevier, vol. 219(1), pages 171-200.
    2. Marine Carrasco & Mohamed Doukali, 2022. "Testing overidentifying restrictions with many instruments and heteroscedasticity using regularised jackknife IV [Specification testing in models with many instruments]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 71-97.
    3. Qingliang Fan & Zijian Guo & Ziwei Mei, 2022. "A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates," Papers 2205.00171, arXiv.org, revised Mar 2023.

    More about this item

    Keywords

    Many Instruments; Mean Square Error; Jackknife; Regularization Methods.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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