论文标题
连续蒙特卡洛与模型回火
Sequential Monte Carlo With Model Tempering
论文作者
论文摘要
现代的宏观工程学通常依赖于时间序列模型，以评估可能性函数很耗时。我们证明了如何通过使用Sequential Monte Carlo（SMC）算法从感兴趣的模型中重新加权和突变后绘制此类模型的贝叶斯计算如何通过重新加权和突变后绘制。我们将该技术应用于具有随机波动性和非线性动态随机通用平衡模型的矢量自动进度的估计。我们获得的运行时减少量从27％到88％。
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%.
