activity
20192023
most citedBinary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness

4 citations · 7 across the 7 of their papers we have counts for

collaborators

7 papers

econ.EM2023★ 2 cited

Econometrics of Machine Learning Methods in Economic Forecasting

Andrii Babii, Eric Ghysels, Jonas Striaukas

This paper surveys the recent advances in machine learning method for economic forecasting. The survey covers the following topics: nowcasting, textual data, panel and tensor data,…

econ.EM2023

Panel Data Nowcasting: The Case of Price-Earnings Ratios

Andrii Babii, Ryan T. Ball, Eric Ghysels +1

The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting…

econ.EM2022

Tensor PCA for Factor Models

Andrii Babii, Eric Ghysels, Junsu Pan

Modern empirical analysis often relies on high-dimensional panel datasets with non-negligible cross-sectional and time-series correlations. Factor models are natural for capturing…

econ.EM2020★ 4 cited

Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness

Andrii Babii, Xi Chen, Eric Ghysels +1

We study the binary choice problem in a data-rich environment with asymmetric loss functions. The econometrics literature covers nonparametric binary choice problems but does not o…

econ.EM2020★ 1 cited

Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application

Andrii Babii, Ryan T. Ball, Eric Ghysels +1

The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at different frequencies. We focus on the sparse-group LASSO…

econ.EM2019

High-Dimensional Granger Causality Tests with an Application to VIX and News

Andrii Babii, Eric Ghysels, Jonas Striaukas

We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation co…

q-fin.PM2019

Artificial Intelligence Alter Egos: Who benefits from Robo-investing?

Catherine D'Hondt, Rudy De Winne, Eric Ghysels +1

Artificial intelligence, or AI, enhancements are increasingly shaping our daily lives. Financial decision-making is no exception to this. We introduce the notion of AI Alter Egos,…