13 citations · 27 across the 10 of their papers we have counts for
13 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…
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…
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…
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…
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 mixed-frequency IV regression
Andrii Babii
This paper introduces a high-dimensional linear IV regression for the data sampled at mixed frequencies. We show that the high-dimensional slope parameter of a high-frequency covar…
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…
Isotonic Regression Discontinuity Designs
Andrii Babii, Rohit Kumar
This paper studies the estimation and inference for the isotonic regression at the boundary point, an object that is particularly interesting and required in the analysis of monoto…
Is completeness necessary? Estimation in nonidentified linear models
Andrii Babii, Jean-Pierre Florens
Modern data analysis depends increasingly on estimating models via flexible high-dimensional or nonparametric machine learning methods, where the identification of structural param…
Are Unobservables Separable?
Andrii Babii, Jean-Pierre Florens
It is common to assume in empirical research that observables and unobservables are additively separable, especially, when the former are endogenous. This is done because it is wid…
Honest Confidence Sets in Nonparametric IV Regression and Other Ill-Posed Models
Andrii Babii
This paper develops inferential methods for a very general class of ill-posed models in econometrics encompassing the nonparametric instrumental variable regression, various functi…