Forecasting US inflation using Bayesian nonparametric models Page 1 The Annals of
Applied Statistics 2024, Vol. 18, No. 2, 1421–1444 https://doi.org/10.1214/23-AOAS1841 © …

We develop Bayesian neural networks (BNNs) that permit to model generic nonlinearities
and time variation for (possibly large sets of) macroeconomic and financial variables. From a …

Bayesian vector autoregressions (BVARs) are the workhorse in macroeconomic forecasting.
Research in the last decade has established the importance of allowing time-varying …

Predicting the future is notoriously difficult, even more so during crises when the realizations
of economic variables are far from their average. In fact, econometric models are typically …

This paper presents a grid-based algorithm using Gaussian Processes to predict outputs
using spatially and temporally dependent data. First, independent Gaussian Processes are …

We propose and discuss Bayesian machine learning methods for mixed data sampling
(MIDAS) regressions. This involves handling frequency mismatches with restricted and …

Increases in the use of Bayesian inference in applied analysis, the complexity of estimated
models, and the popularity of efficient Markov chain Monte Carlo (MCMC) inference under …

Time-Varying Parameters Vector Autoregressive (TVP-VAR) models are frequently used in
economics to capture evolving relationships among the macroeconomic variables. However …

The inherent feature of Bayesian empirical analysis is the dependence of posterior inference
on prior parameters, which researchers typically specify. However, quantifying the …