2 citations · 5 across the 8 of their papers we have counts for
9 papers
Factor-Augmented Machine Learning Panel Regressions
Andrii Babii, Luca Barbaglia, Eric Ghysels +1
This paper develops the asymptotic theory for high-dimensional panel data regressions in settings with cross-sectionally dependent errors driven by common shocks. We consider a fac…
Nowcasting and aggregation: Why small Euro area countries matter
Andrii Babii, Luca Barbaglia, Eric Ghysels +1
The paper studies the nowcasting of Euro area Gross Domestic Product (GDP) growth using mixed data sampling machine learning panel data regressions with both standard macro release…
High-dimensional censored MIDAS logistic regression for corporate survival forecasting
Wei Miao, Jad Beyhum, Jonas Striaukas +1
This paper addresses the challenge of forecasting corporate distress, a problem marked by three key statistical hurdles: (i) right censoring, (ii) high-dimensional predictors, and…
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,…
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…
Testing for sparse idiosyncratic components in factor-augmented regression models
Jad Beyhum, Jonas Striaukas
We propose a novel bootstrap test of a dense model, namely factor regression, against a sparse plus dense alternative augmenting model with sparse idiosyncratic components. The asy…
Factor-augmented sparse MIDAS regressions with an application to nowcasting
Jad Beyhum, Jonas Striaukas
This article investigates factor-augmented sparse MIDAS (Mixed Data Sampling) regressions for high-dimensional time series data, which may be observed at different frequencies. Our…
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…
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…