Improved Frequentist Prediction Intervals for Autoregressive Models by Simulation
Helske, J., & Nyblom, J. (2015). Improved Frequentist Prediction Intervals for Autoregressive Models by Simulation. In S. J. Koopman, & N. Shephard (Eds.), Unobserved Components and Time Series Econometrics (pp. 291-309). Oxford University Press.
© 2015 Oxford University Press. This is a final draft version of an article whose final and definitive form has been published by OUP. Published in this repository with the kind permission of the publisher.
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 experiments show that the formulas borrowed from the ordinary linear regression theory yield one-step prediction intervals, which have coverage probabilities very close to what is claimed. From a Bayesian point of view the resulting intervals are posterior predictive intervals when uniform priors are assumed for both autoregressive coefficients and logarithm of the disturbance variance. This finding opens the path how to treat multi-step prediction intervals which are obtained easily by simulation either directly from the posterior distribution or using importance sampling. A notable improvement is gained in frequentist coverage probabilities. An application of the method to forecasting the annual gross domestic product growth in the United Kingdom and Spain is given for the period 2002–2011 using the estimation period 1962–2001. ...
PublisherOxford University Press
Is part of publicationUnobserved Components and Time Series Econometrics, Ed. by S. J. Koopman, & N. Shephard. Oxford University Press, ISBN 978-0-19-968366-6
MetadataShow full item record
Showing items with similar title or keywords.
Helske, Jouni; Nyblom, Jukka (Vaasan Yliopisto, 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 ...
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 ...
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 ...
Comparison of the effects of high-intensity interval running, high-intensity interval circuit training and steady-state running on body composition and glucose tolerance in recreationally active adults Kari, Aino (2015)Introduction. The measurement of body composition is important for several reasons, but nowadays when obesity and overweight are common problems all over the world even more attention should be given to body composition. ...