Robinson, Peter M. (1997) Large-sample inference for nonparametric regression with dependent errors. Econometrics; EM/1997/336, EM/1997/336. Suntory and Toyota International Centres for Economics and Related Disciplines, London School of Economics and Political Science, London, UK.Full text not available from this repository.
A central limit theorem is given for certain weighted sums of a covariance stationary process, assuming it is linear in martingale differences, but without any restriction on its spectrum. We apply the result to kernel nonparametric fixed-design regression, giving a single central limit theorem which indicates how error spectral behaviour at only zero frequency influences the asymptotic distribution, and covers long range, short range, and negative dependence. We show how the regression estimates can be studentized in the absence of previous knowledge of which form of dependence regime pertains, and show also that a simpler studentization is possible when long-range dependence can be taken for granted.
|Item Type:||Monograph (Discussion Paper)|
|Additional Information:||© 1997 the author|
|Uncontrolled Keywords:||central limit theorem; nonparametric regression; autocorrelation; long-range dependence|
|Library of Congress subject classification:||H Social Sciences > HB Economic Theory|
|Sets:||Collections > Economists Online
Departments > Economics
Research centres and groups > Suntory and Toyota International Centres for Economics and Related Disciplines (STICERD)
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