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.

翻译：近年来，利用分析趋势、情绪或新闻的数据库进行经济预测或创建指标的做法已获得显著普及，特别是借助Google Trends平台。本文探讨了利用谷歌搜索数据开发新指数的潜力，旨在改进经济预测，尤其关注经济活动中的关键组成部分之一：私人消费（占秘鲁GDP的64%）。通过筛选和估计分类变量，应用机器学习技术，证明了谷歌数据能够识别模式以实时生成领先指标，并提高预测的准确性。最后，结果表明，谷歌的“食品”和“旅游”类别显著降低了预测误差，凸显了以细分方式利用此类信息以改进宏观经济预测的重要性。