Improved frequentist prediction intervals for ARMA models by simulation
Helske, J., & Nyblom, J. (2014). Improved frequentist prediction intervals for ARMA models by simulation. In J. Knif, & B. Pape (Eds.), Contributions to Mathematics, Statistics, Econometrics, and Finance : essays in honour of professor Seppo Pynnönen (pp. 71-86). Vaasan Yliopisto. Acta Wasaensia, 296. http://www.uva.fi/materiaali/pdf/isbn_978-952-476-523-7.pdf
Published in
Acta WasaensiaDate
2014[Introduction] In a traditional approach to time series forecasting, prediction intervals are usually
computed as if the chosen model were correct and the parameters of the model completely
known, with no reference to the uncertainty regarding the model selection
and parameter estimation. The parameter uncertainty may not be a major source
of prediction errors in practical applications, but its effects can be substantial if the
series is not too long. The problems of interval prediction are discussed in depth in
Chatfield (1993, 1996) and Clements & Hendry (1999). [Continues; please see the article]
Publisher
Vaasan YliopistoParent publication ISBN
978-952-476-522-0Is part of publication
Contributions to Mathematics, Statistics, Econometrics, and Finance : essays in honour of professor Seppo PynnönenISSN Search the Publication Forum
0355-2667Keywords
Original source
http://www.uva.fi/materiaali/pdf/isbn_978-952-476-523-7.pdfPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/24478505
Metadata
Show full item recordCollections
Related items
Showing items with similar title or keywords.
-
Improved Frequentist Prediction Intervals for Autoregressive Models by Simulation
Helske, Jouni; Nyblom, Jukka (Oxford University Press, 2015)It is well known that the so called plug-in prediction intervals for autoregressive processes, with Gaussian disturbances, are too narrow, i.e. the coverage probabilities fall below the nominal ones. However, simulation ... -
Predicting Running Performance and Adaptations from Intervals at Maximal Sustainable Effort
Nuuttila, Olli-Pekka; Matomäki, Pekka; Kyröläinen, Heikki; Nummela, Ari (Georg Thieme Verlag KG, 2023)This study examined the predictive quality of intervals performed at maximal sustainable effort to predict 3-km and 10-km running times. In addition, changes in interval performance and associated changes in running ... -
Modeling biomass char gasification kinetics for improving prediction of carbon conversion in a fluidized bed gasifier
Kramb, Jason; Konttinen, Jukka; Gómez-Barea, Alberto; Moilanen, Antero; Umeki, Kentaro (Elsevier Ltd, 2014)Gasification of biomass in a fluidized bed (FB) was modeled based on kinetic data obtained from previously conducted thermo- gravimetic analysis. The thermogravimetric analysis experiments were designed to closely resemble ... -
Sauna bathing is associated with reduced cardiovascular mortality and improves risk prediction in men and women: a prospective cohort study
Laukkanen, Tanjaniina; Kunutsor, Setor K.; Khan, Hassan; Willeit, Peter; Zaccardi, Francesco; Laukkanen, Jari (BioMed Central, 2018)Background: Previous evidence indicates that sauna bathing is related to a reduced risk of fatal cardiovascular disease (CVD) events in men. The aim of this study was to investigate the relationship between sauna habits ... -
Airborne-laser-scanning-derived auxiliary information discriminating between broadleaf and conifer trees improves the accuracy of models for predicting timber volume in mixed and heterogeneously structured forests
Bont, Leo Gallus; Hill, Andreas; Waser, Lars T.; Bürgi, Anton; Ginzler, Christian; Blattert, Clemens (Elsevier, 2020)Managing forests for ecosystem services and biodiversity requires accurate and spatially explicit forest inventory data. A major objective of forest management inventories is to estimate the standing timber volume for ...