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Estimating Macroeconomic Models: A Likelihood Approach

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Author Info
JESUS FERNANDEZ-VILLAVERDE
JUAN F. RUBIO-RAMIREZ

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Abstract

This paper shows how particle filtering facilitates likelihood-based inference in dynamic macroeconomic models. The economies can be non-linear and/or non-normal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those characterizing preferences and technology, and to compare different economies. Both tasks can be implemented from either a classical or a Bayesian perspective. We illustrate the technique by estimating a business cycle model with investment-specific technological change, preference shocks, and stochastic volatility. Copyright 2007 The Review of Economic Studies Limited.

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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-937X.2007.00437.x
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Article provided by Blackwell Publishing in its journal Review of Economic Studies.

Volume (Year): 74 (2007)
Issue (Month): 4 (October)
Pages: 1059-1087
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Handle: RePEc:bla:restud:v:74:y:2007:i:4:p:1059-1087

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