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Bootstrap Inference on Partially Linear Binary Choice Model

Wenzheng Gao, Zhenting Sun

arXiv 30 Nov 2023 · Econometrics

arXiv:2311.18759 · PDF · DOI · OpenAlex · Extracted main text

Abstract

The partially linear binary choice model can be used for estimating structural equations where nonlinearity may appear due to diminishing marginal returns, different life cycle regimes, or hectic physical phenomena. The inference procedure for this model based on the analytic asymptotic approximation could be unreliable in finite samples if the sample size is not sufficiently large. This paper proposes a bootstrap inference approach for the model. Monte Carlo simulations show that the proposed inference method performs well in finite samples compared to the procedure based on the asymptotic approximation.

Citation extraction

11
references
29
in-text mentions
11
distinct cited
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self-citations
3,095
main-text words

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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
1Krief, J. M (2014) An integrated kernel-weighted smoothed maximum score estimator for the partially linear binary response model1.000123100%
2Horowitz, J. L (2002) Bootstrap critical values for tests based on the smoothed maximum score estimator1.00063100%
3Cao, X., X. Chen, W. Gao, and C. Hsiao (2021) Smoothed maximum score estimation with nonparametrically generated covariates0.73732100%
4Blundell, R. W. and J. L. Powell (2004) Endogeneity in semiparametric binary response models0.40511100%
5Chen, S. and H. Zhang (2015) Binary quantile regression with local polynomial smoothing0.40511100%
6Giacomini, R., D. N. Politis, and H. White (2013) A warp-speed method for conducting monte carlo experiments involving bootstrap estimators0.40511100%
7Horowitz, J. L (1992) A smoothed maximum score estimator for the binary response model0.40511100%
8Li, Q. and J. S. Racine (2006) Nonparametric Econometrics: Theory and Practice0.40511100%
9Müller, H (1984) Smooth optimum kernel estimators of densities, regression curves and modes0.40511100%
10Pagan, A. and A. Ullah (1999) Nonparametric Econometrics0.40511100%

Showing the top 10 of 11 scored citations.