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Adaptive estimation in time serise regression models with heteroskedasticity of unknown form

By Javier Hidalgo

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

Topics: Q Science (General), QA Mathematics
Publisher: Cambridge University Press
Year: 1992
DOI identifier: 10.1017/S0266466600012743
OAI identifier: oai:eprints.lse.ac.uk:35720
Provided by: LSE Research Online
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