Bootstrap Inference on Partially Linear Binary Choice Model

Wenzheng Gao and Zhenting Sun

Papers from arXiv.org

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.

Date: 2023-11
New Economics Papers: this item is included in nep-dcm and nep-ecm
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