Ramírez Hassan, Andrés2016-03-082015http://hdl.handle.net/10784/8156In this paper we examine which macroeconomic and financial variables have most predictive power for the target repo rate decisions made by the Federal Reserve -- We conduct the analysis for the FOMC decisions during the period June 1998-April 2015 using dynamic logistic models with dynamic Bayesian Model Averaging that allows to perform predictions in real-time with great flexibility -- The computational burden of the algorithm is reduced by adapting a Markov Chain Monte Carlo Model Composition: MC3 -- We found that the outcome of the FOMC meetings during the sample period are predicted well: Logistic DMA-Up and Dynamic Logit-Up models present hit ratios of 87,2 and 88,7; meanwhile, hit ratios for the Logistic DMA-Down and Dynamic Logit-Down models are 79,8 and 68,0, respectivelyspamasterThesisinfo:eu-repo/semantics/openAccessMACROECONOMÍATEORÍA BAYESIANA DE DECISIONES ESTADÍSTICASPREDICCIONESPROCESOS DE MARKOVINCERTIDUMBRE (ECONOMÍA)MODELOS ECONOMÉTRICOSMÉTODO DE MONTECARLOMacroeconomicsBayesian statistical decision theoryForecastingMarkov processesUncertaintyEconometric modelsMonte carlo methodAcceso abierto2016-03-08Alzate Arias, Hernán Alonso