This paper proposes an approach to marginal likelihood approximation for nonlinear state space models that makes Bayesian model comparison possible in large samples.

This paper proposes dynamic factor models for matrix-valued time series useful for empirical macroeconomics and financial economics.

This paper applies dynamic factor models for matrix-valued time series on euro area inflation panel and develops inflationary pressure indices for eura area countries and extend the model to estimate missing data employing the matrix structure.

This paper develops a marginal likelihood estimator that combines importance sampling and variational approximation for comparing large VARs with different time-varying volatility specifications and outlier adjustments.

We develop an asymmetric dynamic factor model that allows the impact of latent factors to vary depending on whether the factors are larger or smaller than their thresholds.