Simulation-Based Density Estimation for Time Series Using Covariate Data
提出一种利用协变量信息的模拟密度估计方法，适用于多种时间序列参数模型，并推导了渐近性质，在计量和金融应用中表现良好。
This article proposes a simulation-based density estimation technique for time series that exploits information found in covariate data. The method can be paired with a large range of parametric models used in time series estimation. We derive asymptotic properties of the estimator and illustrate attractive finite sample properties for a range of well-known econometric and financial applications.