EconBase
← All papers

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

Andrii Babii, Ryan T. Ball, Eric Ghysels, Jonas Striaukas

arXiv 8 Aug 2020 · Econometrics · publishedJournal of Econometrics (2022) · 35 citations (OpenAlex)

arXiv:2008.03600 · PDF · DOI · OpenAlex · Extracted main text

Abstract

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 regularization. This type of regularization can take advantage of the mixed frequency time series panel data structures and improve the quality of the estimates. We obtain oracle inequalities for the pooled and fixed effects sparse-group LASSO panel data estimators recognizing that financial and economic data can have fat tails. To that end, we leverage on a new Fuk-Nagaev concentration inequality for panel data consisting of heavy-tailed $\tau$-mixing processes.

Citation extraction

44
references
83
in-text mentions
44
distinct cited
3
self-citations
10,429
main-text words

appendix boundary found by appendix_command · 55% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Babii, Ghysels, and Striaukas (2021) Machine learning time series regressions with an application to nowcasting self0.88810470%
2Bybee, Kelly, Manela, and Xiu (2019) The structure of economic news0.87472100%
3Ball and Ghysels (2018) Automated earnings forecasts: beat analysts or combine and conquer?0.81142100%
4Foroni, Marcellino, and Schumacher (2015) Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials0.73732100%
5Babii, Ghysels, and Striaukas (2021) High-dimensional Granger causality tests with an application to VIX and news self0.71411636%
6Dedecker and Prieur (2004) Coupling for $$-dependent sequences and applications0.6445240%
7Carabias (2018) The real-time information content of macroeconomic news: implications for firm-level earnings expectations0.64422100%
8Dedecker and Doukhan (2003) A new covariance inequality and applications0.5112250%
9Phillips and Moon (1999) Linear regression limit theory for nonstationary panel data0.5112250%
10Dedecker and Prieur (2005) New dependence coefficients. Examples and applications to statistics0.51121100%

Showing the top 10 of 44 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Nowcasting and aggregation: Why small Euro area countries matter1.00084
2Panel Data Nowcasting: The Case of Price-Earnings Ratios1.00073
3High-dimensional censored MIDAS logistic regression for corporate survival forecasting0.94163
4Factor-Augmented Machine Learning Panel Regressions0.64422
5Econometrics of Machine Learning Methods in Economic Forecasting0.51121
6Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions0.40511
7High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods0.40511
8High-Dimensional Tail Index Regression0.40511
9Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity0.40511