| Abstract: |
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’ 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. |