In the context of time series regression, we extend the standard Tobitmodel to allow for the possibility of conditional heteroskedastic error processes of the GARCH type.We discuss the likelihood function of the Tobit model in the presence of conditionally heteroskedastic errors.Expressing the exact likelihood function turns out to be infeasible, and we propose anapproximation by treating the model as being conditionally Gaussian. The performance of theestimator is investigated by means of Monte Carlo simulations. We find that, when the errorterms follow a GARCH process, the proposed estimator considerably outperforms the standardTobit quasi maximum likelihood estimator. The efficency loss due to the approximationof the likelihood is finally evaluated.
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Paper provided by Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) in its series Working Papers. Serie AD with number
1997-13.
Find related papers by JEL classification: C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models
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Gourieroux, C & Monfort, A & Renault, E, 1993.
"Indirect Inference,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 8(S), pages S85-118, Suppl. De.
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Gourieroux, C. & Monfort, A. & Renault, E., 1992.
"Indirect Inference,"
Papers
92.279, Toulouse - GREMAQ.