In this paper, we survey the most recent advances in supervised machine learning (ML) and
high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear …

The main goal of this article is to provide an answer to the question: does anything forecast
exchange rates, and if so, which variables? It is well known that exchange rate fluctuations …

In a capitalist system, consumers, investors, and corporations orient their activities toward a
future that contains opportunities and risks. How actors assess uncertainty is a problem that …

The evaluation of macroeconomic policy decisions has traditionally relied on the formulation
of a specific economic model. In this work, we show that two statistics are sufficient to detect …

We address some of the key questions that arise in forecasting the price of crude oil. What
do applied forecasters need to know about the choice of sample period and about the …

We examine the behavior of mortgage borrowers over several economic cycles using an
unprecedented dataset of origination and monthly performance records for over 120 million …

We identify networks of volatility spillovers and examine time-varying spillover intensities
with daily implied volatilities of US Treasury bonds, global stock indices, and commodities …

Despite their widespread use as predictors of the spot price of oil, oil futures prices tend to
be less accurate in the mean‐squared prediction error sense than no‐change forecasts …

We propose a method for comparing density forecasts that is based on weighted versions of
the continuous ranked probability score. The weighting emphasizes regions of interest, such …

This paper studies a quantile regression dynamic panel model with fixed effects. Panel data
fixed effects estimators are typically biased in the presence of lagged dependent variables …