Abstract
Bayesian inference in economics is primarily perceived as a methodology for cases where the data are short, that is, not informative enough in order to be able to obtain reliable econometric estimates of quantities of interest. In these cases, prior beliefs, such as the experience of the decision-maker or results from economic theory, can be explicitly incorporated to the econometric estimation problem and enhance the desired solution.
In contrast, in fields such as computing science and signal processing Bayesian inference and computation has long been used for tackling challenges associated with ultra high-dimensional data. Such fields have developed several novel Bayesian algorithms that have gradually been established in mainstream statistics, and they now have a prominent position in machine learning applications in numerous disciplines.
While traditional Bayesian algorithms are powerful enough in order to allow estimation of very complex problems (for instance, nonlinear dynamic stochastic general equilibrium models) they are not able to cope computationally with the demands of rapidly increasing economic datasets. Bayesian machine learning algorithms are able to provide rigorous and computationally feasible solutions to various high-dimensional econometric problems, thus, supporting modern decision-making in a timely manner.