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Adaptive Estimation in Time Serise Regression Models With Heteroskedasticity of Unknown Form

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  • Hidalgo, Javier

Abstract

In a multiple time series regression model the residuals are heteroskedastic and serially correlated of unknown form. GLS estimates of the regression coefficients using kernel regression and spectral methods are shown to be adaptive, in the sense of having the same asymptotic distribution, to the first order, as GLS estimates based on knowledge of the actual heteroskedasticity and serial correlation. A Monte Carlo experiment about the performance of our estimator is described.

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  • Hidalgo, Javier, 1992. "Adaptive Estimation in Time Serise Regression Models With Heteroskedasticity of Unknown Form," Econometric Theory, Cambridge University Press, vol. 8(2), pages 161-187, June.
    • Handle: RePEc:cup:etheor:v:8:y:1992:i:02:p:161-187_01
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    Cited by:

    1. Robinson, Peter M. & Thawornkaiwong, Supachoke, 2012. "Statistical inference on regression with spatial dependence," Journal of Econometrics, Elsevier, vol. 167(2), pages 521-542.
    2. Dennis Kristensen, 2009. "Semiparametric modelling and estimation (in Russian)," Quantile, Quantile, issue 7, pages 53-83, September.
    3. Nilanjana Roy, 2002. "Is Adaptive Estimation Useful For Panel Models With Heteroskedasticity In The Individual Specific Error Component? Some Monte Carlo Evidence," Econometric Reviews, Taylor & Francis Journals, vol. 21(2), pages 189-203.
    4. Dennis Kristensen, 2009. "Semiparametric Modelling and Estimation: A Selective Overview," CREATES Research Papers 2009-44, Department of Economics and Business Economics, Aarhus University.

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