Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/130012
Authors: 
Chen, Xiaohong
Christensen, Timothy M.
Year of Publication: 
2014
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP46/14
Abstract: 
We show that spline and wavelet series regression estimators for weakly dependent regressors attain the optimal uniform (i.e. sup-norm) convergence rate (n= log n)..p=(2p+d) of Stone (1982), where d is the number of regressors and p is the smoothness of the regression function. The optimal rate is achieved even for heavy-tailed martingale difference errors with finite (2 + (d=p))th absolute moment for d=p < 2.We also establish the asymptotic normality of t statistics for possibly nonlinear, irregular functionals of the conditional mean function under weak conditions. The results are proved by deriving a new exponential inequality for sums of weakly dependent random matrices, which is of independent interest.
Subjects: 
Nonparametric series regression
Optimal uniform convergence rates
Weak dependence
Random matrices
Splines
Wavelets
(Nonlinear) Irregular Functionals
Sieve t statistics
JEL: 
C12
C14
C32
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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