Open AccessPRONÓSTICOS BAYESIANOS USANDO SIMULACIÓN ESTOCÁSTICA*
Author(s) -
David F. Muñoz Negrón
Publication year - 2009
Publication title -
cuadernos de difusión
Language(s) - English
Resource type - Journals
eISSN - 1815-6606
pISSN - 1815-6592
DOI - 10.46631/jefas.2009.v14n26.01
Subject(s) - estimator , point estimation , bayesian probability , statistics , variance (accounting) , confidence interval , conditional variance , interval estimation , construct (python library) , mathematics , prediction interval , conditional probability distribution , posterior probability , interval (graph theory) , delta method , econometrics , point (geometry) , computer science , autoregressive conditional heteroskedasticity , volatility (finance) , geometry , accounting , combinatorics , business , programming language
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.