Search Results for author: Eric Ghysels

Found 8 papers, 3 papers with code

Econometrics of Machine Learning Methods in Economic Forecasting

no code implementations21 Aug 2023 Andrii Babii, Eric Ghysels, Jonas Striaukas

This paper surveys the recent advances in machine learning method for economic forecasting.

Econometrics Time Series

Panel Data Nowcasting: The Case of Price-Earnings Ratios

no code implementations5 Jul 2023 Andrii Babii, Ryan T. Ball, Eric Ghysels, Jonas Striaukas

The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies.

regression Time Series +1

Tensor Principal Component Analysis

no code implementations26 Dec 2022 Andrii Babii, Eric Ghysels, Junsu Pan

A tensor factor model describes a high-dimensional dataset as a sum of a low-rank component and an idiosyncratic noise, generalizing traditional factor models for panel data.

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

1 code implementation8 Aug 2020 Andrii Babii, Ryan T. Ball, Eric Ghysels, Jonas Striaukas

The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at different frequencies.

BIG-bench Machine Learning Time Series +1

Machine Learning Time Series Regressions with an Application to Nowcasting

2 code implementations28 May 2020 Andrii Babii, Eric Ghysels, Jonas Striaukas

This paper introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies.

BIG-bench Machine Learning Time Series +1

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

1 code implementation13 Dec 2019 Andrii Babii, Eric Ghysels, Jonas Striaukas

We establish the debiased central limit theorem for low dimensional groups of regression coefficients and study the HAC estimator of the long-run variance based on the sparse-group LASSO residuals.

regression Time Series +2

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

no code implementations8 Jul 2019 Catherine D'Hondt, Rudy De Winne, Eric Ghysels, Steve Raymond

We introduce the notion of AI Alter Egos, which are shadow robo-investors, and use a unique data set covering brokerage accounts for a large cross-section of investors over a sample from January 2003 to March 2012, which includes the 2008 financial crisis, to assess the benefits of robo-investing.

Decision Making

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