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 method of model averaging has become an important tool to deal with model
uncertainty, for example in situations where a large amount of different theories exist, as are …

Inflation forecasting is an important but difficult task. Here, we explore advances in machine
learning (ML) methods and the availability of new datasets to forecast US inflation. Despite …

Summary We move beyond Is Machine Learning Useful for Macroeconomic Forecasting? by
adding the how. The current forecasting literature has focused on matching specific …

Much of the extant literature predicts market returns with “simple” models that use only a few
parameters. Contrary to conventional wisdom, we theoretically prove that simple models …

This article introduces structured machine learning regressions for high-dimensional time
series data potentially sampled at different frequencies. The sparse-group LASSO estimator …

In this paper we present and describe a large quarterly frequency, macroeconomic
database. The data provided are closely modeled to that used in Stock and Watson (2012a) …

This paper proposes a novel supervised learning technique for forecasting: scaled principal
component analysis (sPCA). The sPCA improves the traditional principal component …

This paper compares the predictive power of credit scoring models based on machine
learning techniques with that of traditional loss and default models. Using proprietary …

We develop a novel method for the identification of monetary policy shocks. By applying
natural language processing techniques to documents that Federal Reserve staff prepare in …