Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction

Mengheng Li and Siem Jan Koopman

Journal of Applied Econometrics, 2021, vol. 36, issue 5, 614-627

Abstract: 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.

Date: 2021
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