Estimating Macroeconomic Models: A Likelihood Approach
展示粒子滤波如何帮助对动态宏观经济模型进行基于似然的推断，适用于非线性或非正态经济，可从经典或贝叶斯角度估计结构参数并比较不同经济，并用含投资技术变化、偏好冲击和随机波动的商业周期模型示例。
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, Wiley-Blackwell.