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Estimating Macroeconomic Models: A Likelihood Approach

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  • Jesús Fernández-Villaverde
  • Juan F. Rubio-Ramirez

Abstract

This paper shows how particle filtering allows us to undertake likelihood-based inference in dynamic macroeconomic models. The models can be nonlinear 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.

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Bibliographic Info

Paper provided by UCLA Department of Economics in its series Levine's Bibliography with number 122247000000000849.

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Date of creation: 03 Mar 2006
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Handle: RePEc:cla:levrem:122247000000000849

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