DYNAMIC TIME SERIES BINARY CHOICE

Robert M. De Jong, Tiemen Woutersen

Research output: Contribution to journalArticle

44 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)673-702
Number of pages30
JournalEconometric Theory
Volume27
Issue number4
DOIs
StatePublished - Aug 1 2011
Externally publishedYes

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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