Statistical inference for dynamic models in high dimensions often comes along with a huge amount of parameters that need to be estimated. Thus, to handle the curse of dimensionality, suitable regularization methods are of prime importance, and efficient computational tools are required to make practical estimation feasible. In this talk, we exemplify how these two principles can be implemented for models of importance in macroeconomics and finance. First, we discuss a Bayesian vector autoregressive (VAR) model with time-varying contemporaneous correlations that is capable of handling vast dimensional information sets. Second, we propose a straightforward algorithm to carry out inference in large dynamic regression settings with mixture innovation components for each coefficient in the system.
26 Nov. 2018 → 30 Nov. 2018
Bayesians Statistics in the Big Data Era
Österreichische Systematik der Wissenschaftszweige (ÖFOS)