dc.contributor.author | Davidson, James | en_GB |
dc.contributor.author | Hashimzade, Nigar | en_GB |
dc.contributor.department | University of Exeter; Hashimzade now at University of Reading | en_GB |
dc.date.accessioned | 2009-01-27T16:46:56Z | en_GB |
dc.date.accessioned | 2011-01-25T10:25:36Z | en_GB |
dc.date.accessioned | 2013-03-19T15:56:16Z | |
dc.date.issued | 2008-11-19 | en_GB |
dc.description.abstract | The so-called type I and type II fractional Brownian motions are limit distributions associated with the fractional integration model in which pre-sample shocks are either included in the lag structure, or suppressed. There can be substantial differences between the distributions of these two processes and of functionals derived from them, so that it becomes an important issue to decide which model to use as a basis for inference. Alternative methods for simulating the type I case are contrasted, and for models close to the nonstationarity boundary, truncating infinite sums is shown to result in a significant distortion of the distribution. A simple simulation method that overcomes this problem is described and implemented. The approach also has implications for the estimation of type I ARFIMA models, and a new conditional ML estimator is proposed, using the annual Nile minima series for illustration. | en_GB |
dc.identifier.doi | 10.1016/j.csda.2008.11.008 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10036/48077 | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier | en_GB |
dc.subject | fractional Brownian motions | en_GB |
dc.subject | long memory | en_GB |
dc.subject | simulation | en_GB |
dc.subject | ARFIMA | en_GB |
dc.title | Type I and type II fractional Brownian motions: a reconsideration | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2009-01-27T16:46:56Z | en_GB |
dc.date.available | 2011-01-25T10:25:36Z | en_GB |
dc.date.available | 2013-03-19T15:56:16Z | |
dc.description | Authors' draft published as Discussion paper | en_GB |
dc.identifier.journal | Computational Statistics & Data Analysis | en_GB |