Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions

Xiaohong Chen () and Timothy M. Christensen
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Timothy M. Christensen: Institute for Fiscal Studies

No CWP46/14, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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

Date: 2014-12-22
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Related works:
Journal Article: Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions (2015)
Working Paper: Optimal Uniform Convergence Rates and Asymptotic Normality for Series Estimators under Weak Dependence and Weak Conditions (2014)
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