000			01615nam a22002417a 4500
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008			240319b |||||||| |||| 00| 0 eng d
022			_a02664666
040			_aMSU _bEnglish _cMSU _erda
050	0	0	_aHB139.T52 ECO
100	1		_aXu, Ke-li _eauthor
245	1	0	_aNonparametric inference for conditional quantiles of time series _ccreated by Ke-Li Xu
264	1		_aCambridge: _bCambridge University Press, _c2013.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_aEconometric theory _vVolume 29, number 4
520	3		_aThis paper considers model-free hypothesis testing and confidence interval construction for conditional quantiles of time series. A new method, which is based on inversion of the smoothed empirical likelihood of the conditional distribution function around the local polynomial estimator, is proposed. The associated inferential procedures do not require variance estimation, and the confidence intervals are automatically shaped by data. We also construct the bias-corrected empirical likelihood, which does not require undersmoothing. Limit theories are developed for mixing time series. Simulations show that the proposed methods work well in finite samples and outperform the normal confidence interval. An empirical application to inference of the conditional value-at-risk of stock returns is also provided.
650			_aTime series analysis _vNonparametric statistics | _xTheory
856			_u: https://doi.org/10.1017/S0266466612000667
942			_2lcc _cJA
999			_c164424 _d164424