Berichte der Arbeitsgruppe Technomathematik (AGTM Report)
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145
We consider nonparametric estimation of the coefficients a_i(.), i=1,...,p, on a time-varying autoregressive process. Choosing an orthonormal wavelet basis representation of the functions a_i(.), the empirical wavelet coefficients are derived from the time series data as the solution of a least squares minimization problem. In order to allow the a_i(.) to be functions of inhomogeneous regularity, we apply nonlinear thresholding to the empirical coefficients and obtain locally smoothed estimates of the a_i(.). We show that the resulting estimators attain the usual minimax L_2-rates up to a logarithmic factor, simultaneously in a large scale of Besov classes. The finite-sample behaviour of our procedure is demonstrated by application to two typical simulated examples.
182
We develop a test for stationarity of a time series against the alternative of a time-changing covariance structure. Using localized versions of the periodogram, we obtain empirical versions of a reasonable notion of a time-varying spectral density. Coefficients w.r.t. a Haar wavelet series expansion of such a time-varying periodogram are a possible indicator whether there is some deviation from covariance stationarity. We propose a test based on the limit distribution of these empirical coefficients.