000			01461nam a22002297a 4500
003			ZW-GwMSU
005			20240326110105.0
008			240326b |||||||| |||| 00| 0 eng d
040			_aMSU _bEnglish _cMSU _erda
050	0	0	_aHB139.T52 ECO
100	1		_aRobinson, Peter _eauthor
245	1	0	_aInference on nonparametrically trending time series with fractional errors _ccreated by P. M. Robinson
264	1		_aCambridge: _bCambridge University Press, _c2009.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_aEconometric theory _vVolume 25, number 6
520	3		_aThe central limit theorem for nonparametric kernel estimates of a smooth trend, with linearly-generated errors, indicates asymptotic independence and homoscedasticity across fixed points, irrespective of whether disturbances have short memory, long memory, or anti persistence. However, the asymptotic variance depends on the kernel function in a way that varies across these three circumstances, and in the latter two involves a double integral that cannot necessarily be evaluated in closed form. For a particular class of kernels, we obtain analytic formulae. We discuss extensions to more general settings, including ones involving possible cross-sectional or spatial dependence.
650			_aTime series analysis _xEstimation theory
856			_uhttps://doi.org/10.1017/S0266466609990302
942			_2lcc _cJA
999			_c164571 _d164571