Challenges and opportunities for 21st century Bayesian econometricians: A personal view

In this essay six societal challenges and research opportunities that confront 21st century Bayesian econometricians are briefly discussed using an important feature of modern Bayesian econometrics: conditional probabilities of a wide range of economic events of interest can be evaluated by using simulation-based Bayesian inference. The enormous advances in hardware and software have made this Bayesian computational approach a very attractive vehicle of research in many subfields in economics where novel data patterns and substantial model complexity are predominant. In this essay the following challenges and opportunities are briefly discussed, including the scientific results obtained in the 20th century leading up to the following challenges. Posterior and predictive analysis of everything: connecting micro-economic causality with macro-economic issues; in need of speed: model complexity and the golden age of algorithms; learning about models, forecasts and policies including their uncertainty; temporal distributional change due to polarisation, imbalances and shocks; climate change and the macroeconomy; finally and most importantly, widespread, accessible, advanced high-level training.