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Grupos atípicos en modelos econométricos

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  • Justel, Ana
  • Peña, Daniel
  • Sánchez, María Jesús

Abstract

Este trabajo present a una revision de 10s metodos actua1es de detecci6n y tratamiento de grupos de datos atipicos en mode10s econometricos. Cuando existen grupos de va10res atlpicos 10s estadlsticos desarrollados en 10s anos ochenta para datos individua1es no son fiab1es: pueden no identificar conjuntos de atipicos y pueden senalar como atipicos a datos que no 10 son. Este fenomeno es conocido como enmascaramiento. En esta revision se analizan 10s metodos recientes de identificaci6n de grupos de valores atipicos que evitan el enmascaramiento para modelos de regresion estaticos y dinamicos y series tempora1es, tanto desde el punto de vista clasico como bayesiano.

Suggested Citation

  • Justel, Ana & Peña, Daniel & Sánchez, María Jesús, 1994. "Grupos atípicos en modelos econométricos," DES - Documentos de Trabajo. Estadística y Econometría. DS 10755, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:dsrepe:10755
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    References listed on IDEAS

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    1. S. R. Brubacher & G. Tunnicliffe Wilson, 1976. "Interpolating Time Series with Application to the Estimation of Holiday Effects on Electricity Demand," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 25(2), pages 107-116, June.
    2. Pena, Daniel & Ruiz-Castillo, Javier, 1984. "Robust Methods of Building Regression Models-An Application to the Housing Sector," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(1), pages 10-20, January.
    3. Koop, Gary, 1994. "Recent Progress in Applied Bayesian Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 8(1), pages 1-34, March.
    4. Steel, Mark F. J., 1991. "A Bayesian analysis of simultaneous equation models by combining recursive analytical and numerical approaches," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 83-117.
    5. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
    6. Zellner, Arnold, 1988. "Bayesian analysis in econometrics," Journal of Econometrics, Elsevier, vol. 37(1), pages 27-50, January.
    7. Ledolter, Johannes, 1989. "The effect of additive outliers on the forecasts from ARIMA models," International Journal of Forecasting, Elsevier, vol. 5(2), pages 231-240.
    8. Bovas Abraham & George E. P. Box, 1978. "Linear Models and Spurious Observations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(2), pages 131-138, June.
    9. Pena, Daniel, 1990. "Influential Observations in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 235-241, April.
    10. Harvey, Andrew C & Koopman, Siem Jan, 1992. "Diagnostic Checking of Unobserved-Components Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 377-389, October.
    11. Chib, Siddhartha, 1992. "Bayes inference in the Tobit censored regression model," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 79-99.
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