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Generalized linear time series regression


  • Enno Mammen
  • Jens Perch Nielsen
  • Bernd Fitzenberger


We consider a cross-section model that contains an individual component, a deterministic time trend and an unobserved latent common time series component. We show the following oracle property: the parameters of the latent time series and the parameters of the deterministic time trend can be estimated with the same asymptotic accuracy as if the parameters of the individual component were known. We consider this model in two settings: least squares fits of linear specifications of the individual component and the parameters of the deterministic time trend and, more generally, quasilikelihood estimation in a generalized linear time series model. Copyright 2011, Oxford University Press.

Suggested Citation

  • Enno Mammen & Jens Perch Nielsen & Bernd Fitzenberger, 2011. "Generalized linear time series regression," Biometrika, Biometrika Trust, vol. 98(4), pages 1007-1014.
  • Handle: RePEc:oup:biomet:v:98:y:2011:i:4:p:1007-1014

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    References listed on IDEAS

    1. Joel L. Horowitz, 1998. "Bootstrap Methods for Median Regression Models," Econometrica, Econometric Society, vol. 66(6), pages 1327-1352, November.
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    6. Schennach, Susanne M., 2008. "Quantile Regression With Mismeasured Covariates," Econometric Theory, Cambridge University Press, vol. 24(04), pages 1010-1043, August.
    7. Hua Liang & Suojin Wang & Raymond J. Carroll, 2007. "Partially linear models with missing response variables and error-prone covariates," Biometrika, Biometrika Trust, vol. 94(1), pages 185-198.
    8. Purdom Elizabeth & Holmes Susan P, 2005. "Error Distribution for Gene Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-35, July.
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