Published in arXiv preprint, 2026
We propose a unified mixture sampler (UMS) that provides a universal estimation framework for nonlinear state-space models with “exp-exp” likelihood kernels.
Published in arXiv preprint, 2026
We develop a dynamic factor stochastic volatility-in-mean (SVM) specification for vector autoregressions (VARs) that embeds an SVM component within a dynamic factor stochastic volatility structure.
Published in Journal of Econometrics, 2025
In this paper we consider the simulation-based Bayesian analysis of stochastic volatility in mean (SVM) models.
Published in arXiv preprint, 2024
Functional time series data frequently appears in econometric analyses, where the functions of interest are subject to some shape constraints, including monotonicity and convexity, as typical of the estimation of the Lorenz curve. This paper proposes a state-space model for time-varying functions to extract trends and serial dependence from functional time series while imposing the shape constraints on the estimated functions.