Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction
提出一种可行的模拟最大似然方法，用于估计具有随机波动率的不可观测成分时间序列模型，并通过美国通胀数据验证了方法的有效性。
Summary The unobserved components time series model with stochastic volatility has gained much interest in econometrics, especially for the purpose of modelling and forecasting inflation. We present a feasible simulated maximum likelihood method for parameter estimation from a classical perspective. The method can also be used for evaluating the marginal likelihood function in a Bayesian analysis. We show that our simulation‐based method is computationally feasible, for both univariate and multivariate models. We assess the performance of the method in a Monte Carlo study. In an empirical study, we analyse U.S. headline inflation using different univariate and multivariate model specifications.