Insurance regulation often dictates that insurers monitor their solvency risk in real time and take appropriate actions whenever the risk exceeds their tolerance level. Bayesian methods are appealing for prediction problems thanks to their ability to naturally incorporate both sample variability...

This paper constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients and cross-sectional heteroskedasticity. The panel considered in this paper features large cross-sectional dimension (N) but short...

Mixed models are classical tools in statistics for modeling repeated data on subjects, such as data on patients, customers or firms collected over time. These models extend conventional linear models to include latent parameters, called random effects, that capture between-subject variation and...