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dc.contributor.authorOrakanya Kanjanatarakulen_US
dc.contributor.authorPhilai Lertpongpiroonen_US
dc.contributor.authorSombat Singkharaten_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.description.abstract© Springer International Publishing Switzerland 2014. We describe a method for quantifying the uncertainty in statistical forecasts using belief functions. This method consists in two steps. In the estimation step, uncertainty on the model parameters is described by a consonant belief function defined from the relative likelihood function. In the prediction step, parameter uncertainty is propagated through an equation linking the quantity of interest to the parameter and an auxiliary variable with known distribution. This method allows us to compute a predictive belief function that is an alternative to both prediction intervals and Bayesian posterior predictive distributions. In this paper, the feasibility of this approach is demonstrated using a model used extensively in econometrics: linear regression with first order autoregressive errors. Results with macroeconomic data are presented.en_US
dc.subjectComputer Scienceen_US
dc.titleEconometric forecasting using linear regression and belief functionsen_US
dc.typeBook Seriesen_US
article.title.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
article.volume8764en_US Mai Rajabhat Universityen_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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