  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.
