Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Parsimonious Modelling, Testing and Forecasting of Long-Range Dependence in Wind Speed
Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Economics and Statistics.
2004 (English)In: Environmetrics, 2004, 15, 155-171Article in journal (Refereed)
Abstract [en]

Detecting and estimating long-range dependence are important in the analysis of many environmental time series. This article proposes a periodogram roughness (PR) estimator and describes its uses for testing and estimating the dependence structure. Asymptotic critical values are generated for performing the test, and special attention is given to investigating the properties of the PR regarding size and power. The conventional short-memory models, such as the autoregressive (AR), are shown to be less parsimonious. Forecasting errors of both fractional Gaussian noise (FGN) and fractional autoregressive moving average (FARMA) are investigated by conducting simulation studies. In addition to the PR, maximum likelihood (ML) and semi-parametric (SP) estimators are used and evaluated. Our results have shown that more accurate forecasted points are obtained when using the fractional forecasting. The methods are illustrated using Swedish wind speed data

Place, publisher, year, edition, pages
2004.
Keyword [en]
periodogram roughness, long-range dependence, frequency domain, wind speed, fractional forecasting
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:kau:diva-22247OAI: oai:DiVA.org:kau-22247DiVA: diva2:595924
Available from: 2013-01-21 Created: 2013-01-21 Last updated: 2013-01-21

Open Access in DiVA

No full text

Authority records BETA

Almasri, Abdullah

Search in DiVA

By author/editor
Almasri, Abdullah
By organisation
Department of Economics and Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 114 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf