Dynamic time series binary choice
Robert de Jong and
Economics Working Paper Archive from The Johns Hopkins University,Department of Economics
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.
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ets
References: Add references at CitEc
Citations: View citations in EconPapers (28) Track citations by RSS feed
Downloads: (external link)
Our link check indicates that this URL is bad, the error code is: 404 Not Found (http://econ.jhu.edu/wp-content/uploads/pdf/papers/WP538.pdf [301 Moved Permanently]--> http://krieger2.jhu.edu/economics/wp-content/uploads/pdf/papers/WP538.pdf [301 Moved Permanently]--> https://krieger2.jhu.edu/economics/wp-content/uploads/pdf/papers/WP538.pdf)
Journal Article: DYNAMIC TIME SERIES BINARY CHOICE (2011)
Working Paper: Dynamic time series binary choice (2004)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:jhu:papers:538
Access Statistics for this paper
More papers in Economics Working Paper Archive from The Johns Hopkins University,Department of Economics 3400 North Charles Street Baltimore, MD 21218. Contact information at EDIRC.
Bibliographic data for series maintained by None (). This e-mail address is bad, please contact .