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Root-N Consistency Of Intercept Estimators In A Binary Response Model Under Tail Restrictions

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  • Tan, Lili
  • Zhang, Yichong

Abstract

The intercept of the binary response model is not regularly identified (i.e., $\sqrt n$ consistently estimable) when the support of both the special regressor V and the error term ε are the whole real line. The estimator of the intercept potentially has a slower than $\sqrt n$ convergence rate, which can result in a large estimation error in practice. This paper imposes additional tail restrictions which guarantee the regular identification of the intercept and thus the $\sqrt n$-consistency of its estimator. We then propose an estimator that achieves the $\sqrt n$ rate. Last, we extend our tail restrictions to a full-blown model with endogenous regressors.

Suggested Citation

  • Tan, Lili & Zhang, Yichong, 2018. "Root-N Consistency Of Intercept Estimators In A Binary Response Model Under Tail Restrictions," Econometric Theory, Cambridge University Press, vol. 34(6), pages 1180-1206, December.
    • Handle: RePEc:cup:etheor:v:34:y:2018:i:06:p:1180-1206_00
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    Cited by:

    1. Zhewen Pan, 2023. "On semiparametric estimation of the intercept of the sample selection model: a kernel approach," Papers 2302.05089, arXiv.org.
    2. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.

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