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  •   Repositorio Institucional Universidad EAFIT
  • Tesis de Grado
  • Escuela de Economía y Finanzas
  • Maestría en Economía (tesis)
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  •   Repositorio Institucional Universidad EAFIT
  • Tesis de Grado
  • Escuela de Economía y Finanzas
  • Maestría en Economía (tesis)
  • View Item
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Prediction of Federal Funds Target Rate: a dynamic logistic Bayesian Model averaging approach

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Date
2015
Author
Alzate Arias, Hernán Alonso
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Abstract
Abstract
In 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, respectively
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URI
http://hdl.handle.net/10784/8156
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  • Maestría en Economía (tesis) [99]

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