Konferenzpaper

Erschienen in: Compstat 2006 - Proceedings in Computational Statistics

Herausgeberliste: Rizzi, A.; Vichi, M.

Jahr der Veröffentlichung: 2006

Seiten: 483-494

ISBN: 978-3-7908-1708-9

eISBN: 978-3-7908-1709-6

DOI Link: https://doi.org/10.1007/978-3-7908-1709-6_39

Konferenz: Compstat 2006, 17th Symposium

Abstract: 

The convergence of estimators, e.g. maximum likelihood estimators, for increasing sample size is well understood in many cases. However, even when the rate of convergence of the estimator is known, practical application is hampered by the fact, that the estimator cannot always be obtained at tenable computational cost. This paper combines the analysis of convergence of the estimator itself with the analysis of the convergence of stochastic optimization algorithms, e.g. threshold accepting, to the theoretical estimator. We discuss the joint convergence of estimator and algorithm in a formal framework. An application to a GARCH model demonstrates the approach in practice by estimating actual rates of convergence through a large scale simulation study. Despite of the additional stochastic component introduced by the use of an optimization heuristic, the overall quality of the estimates turns out to be superior compared to conventional approaches.

Harvard-Zitierstil: Winker, P. and Maringer, D. (2006) The convergence of optimization based GARCH estimators: theory and application, in Rizzi, A. and Vichi, M. (eds.) Compstat 2006 - Proceedings in Computational Statistics. Heidelberg: Physica-Verlag HD. pp. 483-494. https://doi.org/10.1007/978-3-7908-1709-6_39

APA-Zitierstil: Winker, P., & Maringer, D. (2006). The convergence of optimization based GARCH estimators: theory and application. In Rizzi, A., & Vichi, M. (Eds.), Compstat 2006 - Proceedings in Computational Statistics. (pp. 483-494). Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-1709-6_39