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预期分歧的因子结构AI 解读

We estimate a Bayesian three-dimensional dynamic factor model on the individual forecasts in the Survey of Professional Forecasters. The factors extract the most important dimensions along which disagreement comoves across variables. We interpret our results through a generic dispersed information model. The two most important factors in the data describe disagreement about aggregate supply and demand, respectively. Up until the Great Moderation, supply disagreement was…

DSGE 模型的在线估计AI 解读

Summary This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating dynamic stochastic general equilibrium (DSGE) model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for ‘online’ estimation (that is, re-estimating a model as new data become available), and provide examples of multimodal posteriors that are well captured by SMC…

温度递进粒子滤波AI 解读

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…

多方程马尔可夫切换模型推断的序贯蒙特卡洛方法AI 解读

Summary Vector autoregressions with Markov‐switching parameters (MS‐VARs) offer substantial gains in data fit over VARs with constant parameters. However, Bayesian inference for MS‐VARs has remained challenging, impeding their uptake for empirical applications. We show that sequential Monte Carlo (SMC) estimators can accurately estimate MS‐VAR posteriors. Relative to multi‐step, model‐specific MCMC routines, SMC has the advantages of generality, parallelizability, and…

SUMMARY We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models; wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using two empirical illustrations consisting of the Smets and Wouters model and a larger news shock model we show that the SMC algorithm is better suited for multimodal and irregular posterior distributions than the widely used random…

评估 DSGE 模型对宏观变量共同变动的预测AI 解读

This paper develops and applies tools to assess multivariate aspects of Bayesian Dynamic Stochastic General Equilibrium (DSGE) model forecasts and their ability to predict comovements among key macroeconomic variables. We construct posterior predictive checks to evaluate conditional and unconditional density forecasts, in addition to checks for root-mean-squared errors and event probabilities associated with these forecasts. The checks are implemented on a three-equation…