Bootstrap with larger resample size for root-n consistent density estimation with time series data
Date
2011Author
Chang, Christopher C.Politis, Dimitris Nicolas
Source
Statistics and Probability LettersVolume
81Issue
6Pages
652-661Google Scholar check
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We consider finite-order moving average and nonlinear autoregressive processes with no parametric assumption on the error distribution, and present a kernel density estimator of a bootstrap series that estimates their marginal densities root-n consistently. This is equal to the rate of the best known convolution estimators, and is faster than the standard kernel density estimator. We also conduct simulations to check the finite sample properties of our estimator, and the results are generally better than corresponding results for the standard kernel density estimator. © 2011 Elsevier B.V.