Real-Time Nowcasting of CPI and Food Inflation with Google Trends-Evidence from the US, UK, and India (2012–2025

Author(s)	Mr. Attharva Chawla
Country	India
Abstract	This study develops and evaluates real-time nowcasting models for headline and food inflation using Google Trends data across three major economies: the United States, United Kingdom, and India over the period 2012–2025. Employing mixed-frequency econometric techniques including MIDAS regression and regularized bridge models, we demonstrate that incorporating weekly Google search intensity for inflation-related keywords significantly improves nowcasting accuracy compared to traditional time-series baselines. Our strict real-time evaluation framework accounts for data release lags and information availability constraints, ensuring practical relevance for policymakers. Diebold–Mariano tests with Holm–Bonferroni correction confirm that Google Trends-augmented models systematically outperform seasonal-naive and ARIMA benchmarks, with mean absolute errors reduced by 15-30% across countries. The methodology provides timely inflation signals particularly valuable during periods of economic uncertainty.
Keywords	inflation nowcasting, Google Trends, MIDAS regression, real-time forecasting, mixed-frequency data
Field	Mathematics > Statistics
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-09-11
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.55463

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