Dynamic time series binary choice
AbstractThis paper considers dynamic time series binary choice models. 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. 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. One novel aspect of this paper is a proof that weak dependence properties hold for dynamic binary choice models with correlated errors
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Bibliographic InfoPaper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number 365.
Date of creation: 11 Aug 2004
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binary choice; near epoch dependence; asymptotic theory; smoothed maximum score;
Other versions of this item:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-10-30 (All new papers)
- NEP-DCM-2004-10-30 (Discrete Choice Models)
- NEP-ECM-2004-10-30 (Econometrics)
- NEP-ETS-2004-10-30 (Econometric Time Series)
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