Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/53430
Title: Econometric forecasting using linear regression and belief functions
Authors: Orakanya Kanjanatarakul
Philai Lertpongpiroon
Sombat Singkharat
Songsak Sriboonchitta
Keywords: Computer Science
Mathematics
Issue Date: 1-Jan-2014
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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84921805501&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/53430
ISSN: 16113349
03029743
Appears in Collections:CMUL: Journal Articles

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