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.

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

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.

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.