Efficient Bias Corrected Nonparametric Spectral Estimation
Copyright policy )Efficient Bias Corrected Nonparametric Spectral Estimation
Summary: We consider estimation of a spectral density at a particular frequency. A linear model is developed based on the local second order behaviour of kernel spectral estimates and a second stage estimate is derived from this model. The estimate is shown to be asymptotically more efficient than the kernel estimate with the optimal, but unknown, bandwidth whenever the smoothing parameter is chosen so that it oversmooths the kernel estimate. A small-sample simulation study shows good characteristics of the second stage estimate. Practical implementation is discussed through examples.
- Boston College United States
- University College Dublin Ireland
- University of Massachusetts System United States
linear model, Density estimation, stationary time series, kernel smoothing, local second order behaviour of kernel spectral estimates, small-sample simulation, second stage estimate, spectral density, Inference from stochastic processes and spectral analysis, optimal bandwidth, weak convergence, bias corrected nonparametric spectral estimation
linear model, Density estimation, stationary time series, kernel smoothing, local second order behaviour of kernel spectral estimates, small-sample simulation, second stage estimate, spectral density, Inference from stochastic processes and spectral analysis, optimal bandwidth, weak convergence, bias corrected nonparametric spectral estimation
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