Estimating dynamic equilibrium economies:: Linear versus nonlinear likelihood
Estimating dynamic equilibrium economies:: Linear versus nonlinear likelihood
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DOI：
10.1002/jae.814
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发表时间：
2005-12-01
影响因子：
2.1
通讯作者：
Rubio-Ramírez, JF
中科院分区：
文献类型：
--
作者：
Fernández-Villaverde, J;Rubio-Ramírez, JF
本文比较了动态均衡经济学中进行基于似然性推理的两种方法：顺序蒙特卡洛滤波器和卡尔曼滤波器。序贯蒙特卡罗滤波器利用经济的非线性结构，通过模拟方法评估模型的似然函数。卡尔曼滤波器估计稳定状态下经济的线性化。我们报告了两个主要结果。首先，无论是对于模拟数据还是对于实际数据，顺序蒙特卡罗滤波器都能使模型与数据的拟合效果更好（通过边际似然来衡量）。即使对于近乎线性的情况也是如此。其次，点估计方面的差异虽然绝对值相对较小，但对模型的矩具有重要影响。我们得出的结论是，非线性滤波器是将模型应用于数据的最佳程序。版权所有 (c) 2005 John Wiley & Sons, Ltd.
This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economics: a sequential Monte Carlo filter and the Kalman filter. The sequential Monte Carlo filter exploits the nonlinear structure of the economy and evaluates the likelihood function of the model by simulation methods. The Kalman filter estimates a linearization of the economy around the steady state. We report two main results. First, both for simulated and for real data, the sequential Monte Carlo filter delivers a substantially better fit of the model to the data as measured by the marginal likelihood. This is true even for a nearly linear case. Second, the differences in terms of point estimates, although relatively small in absolute values, have important effects on the moments of the model. We conclude that the nonlinear filter is a superior procedure for taking models to the data. Copyright (c) 2005 John Wiley & Sons, Ltd.