Stationary-increment variance-gamma and \(t\) models: simulation and parameter estimation. (English) Zbl 07882743
Summary: We detail a method of simulating data from long range dependent processes with variance-gamma or \(t\) distributed increments, test various estimation procedures [method of moments (MOM), product-density maximum likelihood (PMLE), non-standard minimum \(\chi^2\) and empirical characteristic function estimation] on the data, and assess the performance of each. The investigation is motivated by the apparent poor performance of the MOM technique using real data [A. Tjetjep and E. Seneta, Int. Stat. Rev. 74, No. 1, 109–126 (2006; Zbl 1131.62096)]; and the need to assess the performance of PMLE for our dependent data models. In the simulations considered the product-density method performs favourably.
© 2008 The Authors. Journal compilation © 2008 International Statistical Institute
© 2008 The Authors. Journal compilation © 2008 International Statistical Institute
Keywords:
variance-gamma model; \(t\) model; simulation; long range dependence; method of moments; product-density maximum likelihood estimation; minimum \(\chi^2\) estimation; empirical characteristic function estimation; skewness; kurtosisCitations:
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