DYNAMIC TIME SERIES BINARY CHOICE
By R. Jong, Tiemen Woutersen. Econometric Theory, 2011
100 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Finance, Marketing
View Paper: https://www.semanticscholar.org/paper/b83ca5ac693e3dd7062ee1acc7ccac3d8216f06f
How researchers see this paper
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In Economics and Econometrics
- Martin P. Weidner
- Koen Jochmans
- Geert Dhaene
- Iv'an Fern'andez-Val
- Jinyong Hahn
- G. Kuersteiner
- Antonio F. Galvao
- H. R. Moon
In Finance
In Marketing
Citations in the corpus
- BIAS REDUCTION FOR DYNAMIC NONLINEAR PANEL MODELS WITH FIXED EFFECTS (2011, background): “For Example 2 we note that De Jong and Woutersen (2011) discuss conditions for consistency such that for wi t = ( 1, yit−1, z′i t )′ we need E[wi tw′i t] nonsingular.”
- Split-Panel Jackknife Estimation of Fixed-Effect Models (2015, background): “Hahn and Kuersteiner (2010, 2011) and de Jong and Woutersen (2011) show that they hold under mild conditions in several popular nonlinear models, including dynamic binary-choice models and dynamic tobit models with exogenous covariates.”