An Artificial Trend Index for Private Consumption Using Google Trends
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915217560043520 |
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| author | Tenorio, Juan Alpiste, Heidi Remón, Jakelin Segil, Arian |
| author_facet | Tenorio, Juan Alpiste, Heidi Remón, Jakelin Segil, Arian |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_21981 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Artificial Trend Index for Private Consumption Using Google Trends Tenorio, Juan Alpiste, Heidi Remón, Jakelin Segil, Arian Econometrics Machine Learning 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. |
| title | An Artificial Trend Index for Private Consumption Using Google Trends |
| topic | Econometrics Machine Learning |
| url | https://arxiv.org/abs/2503.21981 |