The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

In this paper, we assess whether using non-linear dimension reduction techniques pays off
for forecasting inflation in real-time. Several recent methods from the machine learning …

A Bayesian panel vector autoregression to analyze the impact of climate shocks on high-income
economies Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 2, 1543–1573 https://doi.org/10.1214/22-AOAS1681 …

This paper proposes methods for Bayesian inference in time-varying parameter (TVP)
quantile regressions (QRs) featuring conditional heteroskedasticity. I use data augmentation …

The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

Modeling and predicting extreme movements in GDP is notoriously difficult, and the
selection of appropriate covariates and/or possible forms of nonlinearities are key in …

This study investigates the impact of a metric of extreme weather shocks on 32 state-level
inflation rates of the United States (US) over the quarterly period of 1989: 01 to 2017: 04. In …

Time-varying parameter (TVP) models often assume that the TVPs evolve according to a
random walk. This assumption, however, might be questionable since it implies that …

US yield curve dynamics are subject to time‐variation, but there is ambiguity about its
precise form. This paper develops a vector autoregressive (VAR) model with time‐varying …

In this paper, we forecast euro area inflation and its main components using a massive
number of time series on survey expectations obtained from the European Commission's …