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 …

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

This study investigates the effectiveness of sparse regression models with their diverse
specifications and the impulse indicator saturation (IIS) method in forecasting crude oil …

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) …

The modern calculation of textual sentiment involves a myriad of choices as to the actual
calibration. We introduce a general sentiment engineering framework that optimizes the …

Explanatory variables in a predictive regression typically exhibit low signal strength and
various degrees of persistence. Variable selection in such a context is of great importance …

Predicting customer repurchase propensity/frequency has received broad research interests
from marketing, operations research, statistics, and computer science. In the field of …

Volatility forecasts aim to measure future risk and they are key inputs for financial analysis. In
this study, we forecast the realized variance as an observable measure of volatility for …

Tactical sales forecasting is fundamental to production, transportation and personnel
decisions at all levels of a supply chain. Traditional forecasting methods extrapolate …

This paper proposes LASSO estimation specific for panel vector autoregressive (PVAR)
models. The penalty term allows for shrinkage for different lags, for shrinkage towards …