Non-parametric analysis of Granger causality using local measures of divergence
2013 (English)In: Applied Mathematical Sciences, ISSN 1312-885X, Vol. 7, no 83, 4107-4236 p.Article in journal (Refereed) Published
The employment of Granger causality analysis on temporal data is now a standard routine in many scientific disciplines. Since its in- ception, Granger causality has been modeled using a wide variety of analytical frameworks of which, linear models and derivations thereof have been the dominant choice. Nevertheless, a body of research on Granger causality and its applications has focused on non-linear and non-parametric models. One common choice for such models is based on employment of multivariate density estimators and measures of divergence. However, these models are subject to a number of estimations and tuning components that have a great impact on the final outcome. Here we focus on one such general model and improve a number of its tuning bodies. Crucially, we i) investigate the bandwidth selection issue in kernel density estimation, and ii) discuss and propose a solu- tion to the sensitivity of estimated information theoretic measures of divergence to non-linear correspondence. The resulting framework of analysis is evaluated using varied series of simulations.
Place, publisher, year, edition, pages
HIKARI Ltd, , 2013. Vol. 7, no 83, 4107-4236 p.
Probability Theory and Statistics
Research subject Mathematical Statistics
IdentifiersURN: urn:nbn:se:su:diva-101588DOI: 10.12988/ams.2013.35275OAI: oai:DiVA.org:su-101588DiVA: diva2:704403
FunderEU, FP7, Seventh Framework Programme