Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/149592
Authors: 
Jentsch, Carsten
Leucht, Anne
Meyer, Marco
Beering, Carina
Year of Publication: 
2016
Series/Report no.: 
Working Paper Series, Department of Economics, University of Mannheim 16-15
Abstract: 
In this paper, we propose a kernel-type estimator for the local characteristic function of locally stationary processes. Under weak moment conditions, we prove joint asymptotic normality for local empirical characteristic functions. For time-varying linear processes, we establish a central limit theorem under the assumption of finite absolute first moments of the process. Additionally, we prove weak convergence of the local empirical characteristic process. We apply our asymptotic results to parameter estimation. Furthermore, by extending the notion of distance correlation of Szekely, Rizzo and Bakirov (2007) to locally stationary processes, we are able to provide asymptotic theory for local empirical distance correlations. Finally, we provide a simulation study on minimum distance estimation for a-stable distributions and illustrate the pairwise dependence structure over time of log returns of German stock prices via local empirical distance correlations.
Subjects: 
empirical characteristic function
local stationarity
time series
stable distributions
(local) distance correlation
minimum distance estimation
process convergence
asymptotic theory
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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