带模型调和的序贯蒙特卡洛方法
Sequential Monte Carlo With Model Tempering
AI总结：
本文提出利用模型调和的序贯蒙特卡洛算法，将快速近似模型的后验抽样转化为目标模型抽样，显著加速宏观计量模型的贝叶斯估计。
摘要：
现代宏观计量经济学常依赖一些评估似然函数耗时较长的时间序列模型。我们展示了如何利用序贯蒙特卡洛（SMC）算法，将来自可快速评估似然的近似模型的后验抽样，通过重加权与变异，转化为目标模型的后验抽样，从而大幅加速此类模型的贝叶斯计算。我们将该技术应用于估计带随机波动率的向量自回归模型以及一个非线性动态随机一般均衡模型，所得运行时间缩短幅度介于27%至88%之间。
英文摘要：
Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a vector autoregression with stochastic volatility and a nonlinear dynamic stochastic general equilibrium model. The runtime reductions we obtain range from 27% to 88%.
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