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DYNAMIC TIME SERIES BINARY CHOICE

Published online by Cambridge University Press:  03 March 2011

Robert M. de Jong  and
Tiemen Woutersen
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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.

Type
Research Article
Information
Econometric Theory , Volume 27 , Issue 4 , August 2011 , pp. 673 - 702
Copyright
Copyright © Cambridge University Press 2011

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Footnotes

We thank Stephen Cosslett, James Davidson, Jon Faust, Lung-Fei Lee, Benedikt Pötscher, Jim Stock, and Jeff Wooldridge for helpful discussions.

References

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