Pronósticos bayesianos usando simulación estocástica*

• Autor: Muñoz Negrón, David F.
• Formato: artículo
• Fecha de Publicación: 2009
• Institución: Universidad ESAN
• Repositorio: ESAN-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.esan.edu.pe:20.500.12640/2540
• Enlace del recurso: https://revistas.esan.edu.pe/index.php/jefas/article/view/290; https://hdl.handle.net/20.500.12640/2540; https://doi.org/10.46631/jefas.2009.v14n26.01
• Nivel de acceso: acceso abierto
• Materia: Forecasting; Simulation output analysis; Bayesian estimation; Quantile estimation; Pronósticos; Análisis de experimentos por simulación; Estimación bayesiana; Estimación de cuantiles; https://purl.org/pe-repo/ocde/ford#5.02.04

In this article, we present a general framework to construct forecasts using simulation. This framework allows us to incorporate available data into a forecasting model in order to assess parameter uncertainty through a posterior distribution, which is then used to estimate a point forecast in the form of a conditional (given the data) expectation. The uncertainty on the point forecast is assessed through the estimation of a conditional variance and a prediction interval. We discuss how to construct asymptotic confidence intervals to assess the estimation error for the estimators obtained using simulation. We illustrate how this approach is consistent with Bayesian forecasting by presenting two examples, and experimental results that confirm our analytical results are discussed.