What is the effect of sample and prior distributions on a Bayesian autoregressive linear model? An application to piped water consumption

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dc.coverage.spatial Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees eng
dc.creator Ramírez Hassan, Andrés spa
dc.creator Cardona Jiménez, Jhonatan spa
dc.creator Pericchi Guerra, Raul spa
dc.date.accessioned 2014-08-01T20:58:17Z
dc.date.accessioned 2016-06-10T11:45:30Z
dc.date.available 2014-08-01T20:58:17Z
dc.date.available 2016-06-10T11:45:30Z
dc.date.issued 2014-07-23
dc.identifier.uri http://hdl.handle.net/10784/2857
dc.description.abstract In this paper we analyze the effect of four possible alternatives regarding the prior distributions in a linear model with autoregressive errors to predict piped water consumption: Normal-Gamma, Normal-Scaled Beta two, Studentized-Gamma and Student's t-Scaled Beta two. We show the effects of these prior distributions on the posterior distributions under different assumptions associated with the coefficient of variation of prior hyperparameters in a context where there is a conflict between the sample information and the elicited hyperparameters. We show that the posterior parameters are less affected by the prior hyperparameters when the Studentized-Gamma and Student's t-Scaled Beta two models are used. We show that the Normal-Gamma model obtains sensible outcomes in predictions when there is a small sample size. However, this property is lost when the experts overestimate the certainty of their knowledge. In the case that the experts greatly trust their beliefs, it is a good idea to use Student's t distribution as the prior distribution, because we obtain small posterior predictive errors. In addition, we find that the posterior predictive distributions using one of the versions of Student's t as prior are robust to the coefficient of variation of the prior parameters. Finally, it is shown that the Normal-Gamma model has a posterior distribution of the variance concentrated near zero when there is a high level of confidence in the experts' knowledge: this implies a narrow posterior predictive credibility interval, especially using small sample sizes. eng
dc.language.iso eng eng
dc.publisher Universidad EAFIT spa
dc.rights info:eu-repo/semantics/openAccess
dc.title What is the effect of sample and prior distributions on a Bayesian autoregressive linear model? An application to piped water consumption eng
dc.type workingPaper eng
dc.type info:eu-repo/semantics/workingPaper
dc.identifier.jel C11
dc.identifier.jel C53
dc.subject.keyword Autoregressive model eng
dc.subject.keyword Bayesian analysis eng
dc.subject.keyword Forecast eng
dc.subject.keyword Robust prior eng
dc.type.spa Documento de trabajo de investigación spa
dc.type.hasVersion draf eng
dc.rights.accessRights openAccess eng
dc.rights.acceso Libre acceso spa
dc.publisher.department Escuela de Economía y Finanzas spa
dc.contributor.eafitauthor aramir21@eafit.edu.co spa
dc.contributor.eafitauthor jcardonj@dme.ufrj.br spa
dc.contributor.eafitauthor lrpericchi@uprrp.edu spa

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