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Estimating Macroeconomic Models: A Likelihood Approach

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  • Jesus Fernandez-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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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0321.

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Date of creation: Feb 2006
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Handle: RePEc:nbr:nberte:0321

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