Philipp Adämmer, Jan Prüser, Rainer Schüssler
arXiv 27 Feb 2023 · Econometrics · publishedInternational Journal of Forecasting (2024) · 8 citations (OpenAlex)
arXiv:2302.13999 · PDF · DOI · OpenAlex · Extracted main text
We examine the incremental value of news-based data relative to the FRED-MD economic indicators for quantile predictions of employment, output, inflation and consumer sentiment in a high-dimensional setting. Our results suggest that news data contain valuable information that is not captured by a large set of economic indicators. We provide empirical evidence that this information can be exploited to improve tail risk predictions. The added value is largest when media coverage and sentiment are combined to compute text-based predictors. Methods that capture quantile-specific non-linearities produce overall superior forecasts relative to methods that feature linear predictive relationships. The results are robust along different modeling choices.
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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 | Ellingsen, Larsen, and Thorsrud (2022) News media versus FRED-MD for macroeconomic forecasting | 1.000 | 5 | 3 | 100% |
| 2 | McCracken and Ng (2016) FRED-MD: A monthly database for macroeconomic research | 0.928 | 5 | 3 | 80% |
| 3 | Barbaglia, Consoli, and Manzan (2023) Forecasting with economic news | 0.874 | 6 | 2 | 100% |
| 4 | Prüser and Huber (2024) Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions | 0.843 | 3 | 3 | 100% |
| 5 | Larsen and Thorsrud (2019) The value of news for economic developments | 0.843 | 3 | 3 | 100% |
| 6 | Blei and Lafferty (2007) A correlated topic model of science | 0.811 | 4 | 2 | 100% |
| 7 | Meinshausen (2006) Quantile regression forests | 0.737 | 3 | 2 | 100% |
| 8 | Adämmer and Schüssler (2020) Forecasting the equity premium: mind the news! | 0.737 | 3 | 2 | 100% |
| 9 | Clark, Huber, Koop, Marcellino, and Pfarrhofer (2022) Tail forecasting with multivariate Bayesian additive regression trees | 0.737 | 3 | 2 | 100% |
| 10 | Roberts, Stewart, and Airoldi (2016) A model of text for experimentation in the social sciences | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 54 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Transformer-based CoVaR: Systemic Risk in Textual Information | 0.405 | 1 | 1 |