Kastner, Gregor. 2020. Efficient Bayesian Computing in Many Dimensions - Applications in Economics and Finance. BAYESCOMP 2020, Gainesville, Vereinigte Staaten/USA, 07.01.-10.01. Invited Talk

RIS

@CONFERENCE{Kastner2020,
title = {Efficient Bayesian Computing in Many Dimensions - Applications in Economics and Finance},
author = {Gregor Kastner},
year = {2020},
address = {Gainesville},
url = {http://users.stat.ufl.edu/~jhobert/BayesComp2020/Conf_Website/},
language = {EN},
misc = {BAYESCOMP 2020},
note = {Y},
abstract = {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.},
}