Working Paper: CEPR ID: DP5513
Authors: Jess 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.
Keywords: business cycle; dynamic macroeconomic models; nonlinear and/or nonnormal models; particle filtering; stochastic volatility
JEL Codes: C11; C5; E10; E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| stochastic volatility (C58) | economic fluctuations (E32) |
| long-term trends (J11) | decline in aggregate volatility (E32) |
| variations in the volatility of preference shocks (D11) | volatility of growth in US real output per capita (O49) |