Tempered Particle Filtering
Tempered Particle Filtering
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DOI：
10.17016/feds.2016.072
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发表时间：
2016-08
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影响因子：
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通讯作者：
Edward P. Herbst;F. Schorfheide
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文献类型：
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作者：
Edward P. Herbst;F. Schorfheide
非线性状态空间模型的粒子滤波器的精度关键取决于将时间t-1粒子值突变为时间t值的建议分布。在广泛使用的自举粒子滤波器中，该分布由状态转移方程生成。虽然实现起来很简单，但实际性能往往很差。我们开发了一个自校正粒子滤波器，其中的建议分布自适应地构建通过一系列的蒙特卡罗步骤。直观地说，我们从一个方差膨胀的测量误差分布开始，然后通过一系列我们称之为回火的步骤逐渐将方差降低到其标称水平。我们表明，过滤器产生一个无偏的和一致的近似的似然函数。保持运行时间固定，我们的过滤器是更准确的两个DSGE模型的应用程序比引导粒子滤波。
The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t-1 particle values into time t values. In the widely-used bootstrap particle filter this distribution is generated by the state-transition equation. While straightforward to implement, the practical performance is often poor. We develop a self-tuning particle filter in which the proposal distribution is constructed adaptively through a sequence of Monte Carlo steps. Intuitively, we start from a measurement error distribution with an inflated variance, and then gradually reduce the variance to its nominal level in a sequence of steps that we call tempering. We show that the filter generates an unbiased and consistent approximation of the likelihood function. Holding the run time fixed, our filter is substantially more accurate in two DSGE model applications than the bootstrap particle filter.