Sparse Seemingly Unrelated Regression Modelling: Applications in Econometrics and Finance

We present a sparse seemingly unrelated regression (SSUR) model to generate substantively relevant structures in the high-dimensional distributions of seemingly unrelated model (SUR) parameters. This SSUR framework includes prior specifications, posterior computations using Markov chain Monte Carlo methods, evaluations of model uncertainty, and model structure searches. Extensions of the SSUR model to dynamic models embed general structure constraints and model uncertainty in dynamic models. A simulated example illustrates the model and highlights questions regarding model uncertainty, searching, and comparison. The model is then applied to three real-world examples in macroeconomics and finance according to which its identified structures have practical significance.