Andrii Babii, Eric Ghysels, Jonas Striaukas
arXiv 28 May 2020 · Econometrics · publishedJournal of Business and Economic Statistics (2021) · 32 citations (OpenAlex)
arXiv:2005.14057 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroeconomic data may have heavier than exponential tails. An empirical application to nowcasting US GDP growth indicates that the estimator performs favorably compared to other alternatives and that text data can be a useful addition to more traditional numerical data.
appendix boundary found by appendix_titled_section at “Dictionaries \label{appendix:dictionaries}” · 64% of the source is main text. Read the extracted text to check this.
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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bybee, Kelly, Manela, and Xiu (2020) The structure of economic news | 0.843 | 4 | 3 | 75% |
| 2 | Babii, Ghysels, and Striaukas (2020) Inference for high-dimensional regressions with heteroskedasticity and autocorrelation self | 0.830 | 7 | 4 | 57% |
| 3 | Bok, Caratelli, Giannone, Sbordone, and Tambalotti (2018) Macroeconomic nowcasting and forecasting with big data | 0.811 | 4 | 2 | 100% |
| 4 | Dedecker and Prieur (2004) Coupling for $$-dependent sequences and applications | 0.811 | 4 | 2 | 100% |
| 5 | Dedecker and Prieur (2005) New dependence coefficients. Examples and applications to statistics | 0.737 | 3 | 3 | 67% |
| 6 | Quaedvlieg (2019) Multi-horizon forecast comparison | 0.644 | 4 | 1 | 100% |
| 7 | Andreou, Ghysels, and Kourtellos (2013) Should macroeconomic forecasters use daily financial data and how? | 0.644 | 2 | 2 | 100% |
| 8 | Belloni, Chernozhukov, Chetverikov, Hansen, and Kato (2020) High-dimensional econometrics and generalized GMM | 0.644 | 2 | 2 | 100% |
| 9 | Carrasco and Chen (2002) Mixing and moment properties of various GARCH and stochastic volatility models | 0.644 | 2 | 2 | 100% |
| 10 | Chernozhukov, Härdle, Huang, and Wang (2020) Lasso-driven inference in time and space | 0.644 | 2 | 2 | 100% |
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