Dynamic time series binary choice
This paper considers dynamic time series binary choice models. 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' smoothed maximum score estimation of dynamic binary choice models with lags of the dependent variable as regressors. 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. One novel aspect of this paper is a proof that weak dependence properties hold for dynamic binary choice models with correlated errors
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