Arbeitspapier
Dynamic time series binary choice
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.
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 538
- Classification
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Wirtschaft
- Subject
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Zeitreihenanalyse
Probit-Modell
Präferenztheorie
- Event
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Geistige Schöpfung
- (who)
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de Jong, Robert M.
Woutersen, Tiemen
- Event
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Veröffentlichung
- (who)
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The Johns Hopkins University, Department of Economics
- (where)
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Baltimore, MD
- (when)
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2007
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
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Object type
- Arbeitspapier
Associated
- de Jong, Robert M.
- Woutersen, Tiemen
- The Johns Hopkins University, Department of Economics
Time of origin
- 2007
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An exponential class of dynamic binary choice panel data models with fixed effects
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Dynamic mixture models for financial time series
Binary Choice Probabilities on Mixture Sets
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Generalized binary time series models
Analyzing associations in multivariate binary time series
Analyzing Associations in Multivariate Binary Time Series
Time series models for multivariate binary and categorical data
Analyzing dynamic dependencies in time series
Predictions from Binary Choice Models
An exponential class of dynamic binary choice panel data models with fixed effects
An exponential class of dynamic binary choice panel data models with fixed effects
Dynamic mixture models for financial time series