Title
The convergence of estimators based on heuristics: theory and application to a GARCH model
Abstract
Econometric theory describes estimators and their properties, e.g., the convergence of maximum likelihood estimators. However, it is ignored that often the estimators cannot be computed using standard tools, e.g., due to multiple local optima. Then, optimization heuristics might be helpful. The additional random component of heuristics might be analyzed together with the econometric model. A formal framework is proposed for the analysis of the joint convergence of estimator and stochastic optimization algorithm. In an application to a GARCH model, actual rates of convergence are estimated by simulation. The overall quality of the estimates improves compared to conventional approaches.
Year
DOI
Venue
2009
10.1007/s00180-008-0145-5
Computational Statistics
Keywords
DocType
Volume
joint convergence,actual rate,garch · threshold accepting · optimization heuristics · convergence,additional random component,Econometric theory,conventional approach,stochastic optimization algorithm,optimization heuristics,GARCH model,econometric model,formal framework
Journal
24
Issue
ISSN
Citations 
3
1613-9658
5
PageRank 
References 
Authors
0.82
2
2
Name
Order
Citations
PageRank
Peter Winker1285.63
Dietmar Maringer215611.35