Juan Tenorio, Heidi Alpiste, Jakelin Remón, Arian Segil
arXiv 27 Mar 2025 · Econometrics
arXiv:2503.21981 · PDF · DOI · OpenAlex · Extracted main text
In recent years, the use of databases that analyze trends, sentiments or news to make economic projections or create indicators has gained significant popularity, particularly with the Google Trends platform. This article explores the potential of Google search data to develop a new index that improves economic forecasts, with a particular focus on one of the key components of economic activity: private consumption (64% of GDP in Peru). By selecting and estimating categorized variables, machine learning techniques are applied, demonstrating that Google data can identify patterns to generate a leading indicator in real time and improve the accuracy of forecasts. Finally, the results show that Google's "Food" and "Tourism" categories significantly reduce projection errors, highlighting the importance of using this information in a segmented manner to improve macroeconomic forecasts.
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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 | F. X. Diebold, R. S. Mariano, Comparing predictive accuracy, Journal… (1995) 253–263 | 0.644 | 2 | 2 | 100% |
| 2 | R. Giacomini, H. White, Tests of conditional predictive ability, Eco… (2006) 1545–1578 | 0.644 | 2 | 2 | 100% |
| 3 | M. Gil, J. J. Pérez, A. J. Sanchez Fuentes, A. Urtasun, Nowcasting p… (2018) | 0.644 | 2 | 2 | 100% |
| 4 | A. C. Kwan, J. A. Cotsomitis, The usefulness of consumer confidence… (2006) 185–197 | 0.511 | 2 | 1 | 100% |
| 5 | A. D. Aydin, S. C. Cavdar, Comparison of prediction performances of… (2015) 3–14 | 0.405 | 1 | 1 | 100% |
| 6 | O. Barkan, J. Benchimol, I. Caspi, E. Cohen, A. Hammer, N. Koenigste… (2023) 1145–1162 | 0.405 | 1 | 1 | 100% |
| 7 | E. Blanco, Herramientas de big data:?` podemos aprovechar google tre… (2014) | 0.405 | 1 | 1 | 100% |
| 8 | B. Bok, D. Caratelli, D. Giannone, A. M. Sbordone, A. Tambalotti, Ma… (2018) 615–643 | 0.405 | 1 | 1 | 100% |
| 9 | F. Bre, J. M. Gimenez, V. D. Fachinotti, Prediction of wind pressure… (2018) 1429–1441 | 0.405 | 1 | 1 | 100% |
| 10 | F. Camusso, R. Jorge, Google correlate y google trends como herramie… (2021) 26–45 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 31 scored citations.