The advent of massive amounts of textual, audio, and visual data has spurred the
development of econometric methodology to transform qualitative sentiment data into …

The availability of mineral raw materials and their secure and sustainable supply is essential
for the German economy. As a major industrial nation with resource-intensive processing …

We use deep neural networks to estimate an asset pricing model for individual stock returns
that takes advantage of the vast amount of conditioning information, keeps a fully flexible …

External sources of as‐if randomness—that is, external instruments—can be used to identify
the dynamic causal effects of macroeconomic shocks. One method is a one‐step …

We show that machine learning methods, in particular, extreme trees and neural networks
(NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts …

The COVID-19 pandemic has led to enormous data movements that strongly affect
parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To …

Uncertainty about the future rises in recessions. But is uncertainty a source of business
cycles or an endogenous response to them, and does the type of uncertainty matter? We …

In this paper, we use two prevailing shrinkage methods, the lasso and elastic net, to predict
oil price returns with a large set of predictors. The out-of-sample results indicate that the …

Commonly used instruments for the identification of monetary policy disturbances are likely
to combine the true policy shock with information about the state of the economy due to the …

This chapter provides an overview of and user's guide to dynamic factor models (DFMs),
their estimation, and their uses in empirical macroeconomics. It also surveys recent …