论文标题
通过退火进行动态结构宏观限制模型，基于有效的可能性估计
Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models
论文作者
论文摘要
大多数求解的动态结构宏观力模型是具有高维和复杂结构的非线性和/或非高斯州空间模型。我们提出了一种退火受控的顺序蒙特卡洛方法，该方法可提供数值稳定和低方差估计器的可能性函数。该方法依赖于退火程序来逐渐从观测值中引入信息，并通过解决相关的最佳控制问题来构建全球最佳建议分布，从而产生零差异可能性估计器。为了执行参数推断，我们开发了一种新的自适应SMC $^2 $算法，该算法采用了来自退火受控的顺序蒙特卡洛的可能性估计器。我们提供了理论稳定性分析，阐明了我们的方法论的优势以及有关SMC $^2 $估计量的一致性和收敛速率的渐近结果。我们通过估计两个流行的宏观力学模型来说明我们提出的方法论的优势：非线性新凯恩斯主义动态随机通用平衡模型和非线性非高斯基于基于基于高斯的长期长期风险模型。
Most solved dynamic structural macrofinance models are non-linear and/or non-Gaussian state-space models with high-dimensional and complex structures. We propose an annealed controlled sequential Monte Carlo method that delivers numerically stable and low variance estimators of the likelihood function. The method relies on an annealing procedure to gradually introduce information from observations and constructs globally optimal proposal distributions by solving associated optimal control problems that yield zero variance likelihood estimators. To perform parameter inference, we develop a new adaptive SMC$^2$ algorithm that employs likelihood estimators from annealed controlled sequential Monte Carlo. We provide a theoretical stability analysis that elucidates the advantages of our methodology and asymptotic results concerning the consistency and convergence rates of our SMC$^2$ estimators. We illustrate the strengths of our proposed methodology by estimating two popular macrofinance models: a non-linear new Keynesian dynamic stochastic general equilibrium model and a non-linear non-Gaussian consumption-based long-run risk model.
