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DYNAMIC TIME SERIES BINARYCHOICE

Published online by Cambridge University Press:  03 March 2011

Robert M. de Jong  and
Tiemen Woutersen
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Abstract

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This paper considers dynamic time series binary choicemodels. It proves near epoch dependence and strongmixing for the dynamic binary choice model withcorrelated errors. Using this result, it shows in atime series setting the validity of the dynamicprobit likelihood procedure when lags of thedependent binary variable are used as regressors,and it establishes the asymptotic validity ofHorowitz’s smoothed maximum score estimation ofdynamic binary choice models with lags of thedependent variable as regressors. For thesemiparametric model, the latent error is explicitlyallowed to be correlated. It turns out that nolong-run variance estimator is needed for thevalidity of the smoothed maximum score procedure inthe dynamic time series framework.

Information

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

Footnotes

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

References

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