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DYNAMIC TIME SERIES BINARY CHOICE

Robert de Jong and Tiemen Woutersen

Econometric Theory, 2011, vol. 27, issue 04, 673-702

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’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.

Date: 2011
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Related works:
Working Paper: Dynamic time series binary choice (2007) Downloads
Working Paper: Dynamic time series binary choice (2004) Downloads
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