Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

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 …

This paper presents a method for creating machine learning models, specifically a gradient
boosting model and a random forest model, to forecast real GDP growth. This study focuses …

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 …

We compare sparse and dense representations of predictive models in macroeconomics,
microeconomics, and finance. To deal with a large number of possible predictors, we specify …

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 …

The main goal of this paper is to investigate the predictability of five economic uncertainty
indices for oil price volatility in a changing world. We employ the standard predictive …

The combination of forecasts resulting from an ensemble of neural networks has been
shown to outperform the use of a single “best” network model. This is supported by an …

Surveys do! We examine the forecasting power of four alternative methods of forecasting US
inflation out-of-sample: time-series ARIMA models; regressions using real activity measures …

This paper studies two refinements to the method of factor forecasting. First, we consider the
method of quadratic principal components that allows the link function between the …