DYNAMIC TIME SERIES BINARY CHOICE
研究了动态时间序列二元选择模型，证明了动态Probit似然过程和Horowitz平滑最大得分估计在时间序列设定下的有效性，允许潜误差相关且无需长期方差估计。
This paper considers dynamic time series binary choice models. It proves near epoch dependence and strong mixing for the dynamic binary choice model with correlated errors. Using this result, it shows in a time series setting the validity of the dynamic probit likelihood procedure when lags of the dependent binary variable are used as regressors, and it establishes the asymptotic validity of Horowitz’s smoothed maximum score estimation of dynamic binary choice models with lags of the dependent variable as regressors. For the semiparametric model, the latent error is explicitly allowed to be correlated. It turns out that no long-run variance estimator is needed for the validity of the smoothed maximum score procedure in the dynamic time series framework.