Estimating macroeconomic models:: A likelihood approach

• 成果类型: Article
• 署名作者: Fernandez-Villaverde, Jesus; Rubio-Ramirez, Juan F.
• 署名单位: University of Pennsylvania; National Bureau of Economic Research; Duke University; Federal Reserve System - USA; Federal Reserve Bank - Atlanta
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1111/j.1467-937X.2007.00437.x
• 发表日期: 2007
• 页码: 1059-1087
• 关键词: convergence; simulation; moments

摘要

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.