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A tobit model with garch errors

Author

Listed:
  • Gabriele Fiorentini

    () (Universidad de Alicante)

  • Giorgio Calzolari

    (University of Florence)

Abstract

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.

Suggested Citation

  • Gabriele Fiorentini & Giorgio Calzolari, 1997. "A tobit model with garch errors," Working Papers. Serie AD 1997-13, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  • Handle: RePEc:ivi:wpasad:1997-13
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    References listed on IDEAS

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    1. Pesaran, M.H. & Ruge-Murcia, F.J., 1995. "A Discrete-Time Version of Target Zone Models with Jumps," Cahiers de recherche 9530, Universite de Montreal, Departement de sciences economiques.
    2. Hashem Pesaran, M. & Ruge-Murcia, Francisco J., 1996. "Limited-dependent rational expectations models with stochastic thresholds," Economics Letters, Elsevier, vol. 51(3), pages 267-276, June.
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    Cited by:

    1. Juan José Echavarría & Luis Fernando Melo & Santiago Téllez & Mauricio Villamizar, 2013. "The impact of pre-announced day-to-day interventions on the Colombian exchange rate," BIS Working Papers 428, Bank for International Settlements.
    2. Giot, Pierre & Laurent, Sébastien & Petitjean, Mikael, 2010. "Trading activity, realized volatility and jumps," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 168-175, January.
    3. El Ouadghiri, Imane & Uctum, Remzi, 2016. "Jumps in equilibrium prices and asymmetric news in foreign exchange markets," Economic Modelling, Elsevier, vol. 54(C), pages 218-234.
    4. Hsieh, Ping-Hung & Yang, J. Jimmy, 2009. "A censored stochastic volatility approach to the estimation of price limit moves," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 337-351, March.
    5. Slim, Skander & Dahmene, Meriam, 2016. "Asymmetric information, volatility components and the volume–volatility relationship for the CAC40 stocks," Global Finance Journal, Elsevier, vol. 29(C), pages 70-84.
    6. Chen, Ho-Chyuan & Chang, Kuang-Liang & Yu, Shih-Ti, 2012. "Application of the Tobit model with autoregressive conditional heteroscedasticity for foreign exchange market interventions," Japan and the World Economy, Elsevier, vol. 24(4), pages 274-282.
    7. Wei, Steven X., 2002. "A censored-GARCH model of asset returns with price limits," Journal of Empirical Finance, Elsevier, vol. 9(2), pages 197-223, March.
    8. Boffelli, Simona & Urga, Giovanni, 2015. "Macroannouncements, bond auctions and rating actions in the European government bond spreads," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 148-173.

    More about this item

    Keywords

    Censored regressions; conditional heteroskedasticity;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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