Nonparametric adaptive estimation of linear functionals for low frequency observed Lévy processes
AbstractFor a Lévy process X having finite variation on compact sets and finite first moments, µ( dx) = xv( dx) is a finite signed measure which completely describes the jump dynamics. We construct kernel estimators for linear functionals of µ and provide rates of convergence under regularity assumptions. Moreover, we consider adaptive estimation via model selection and propose a new strategy for the data driven choice of the smoothing parameter.
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Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2012-016.
Length: 39 pages
Date of creation: Feb 2012
Date of revision:
Statistics of stochastic processes; Low frequency observed Lévy processes; Nonparametric statistics; Adaptive estimation; Model selection with unknown variance;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-02-27 (All new papers)
- NEP-ECM-2012-02-27 (Econometrics)
- NEP-ETS-2012-02-27 (Econometric Time Series)
- NEP-ORE-2012-02-27 (Operations Research)
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