Abstract
The article is devoted to the analysis of the use of big data technologies to forecast key macroeconomic indicators, such as gross domestic product, inflation, unemployment and consumer consumption. In the context of rapid growth in data volumes, traditional econometric models show significant limitations due to the significant delay of official statistics and the low frequency of their updates. Large amounts of data make it possible to predict the current state of the economy in real time using dynamic factor models, Bayesian methods, lasso-type regularization and neural networks. A literature review shows that the integration of non-traditional sources – banking transactions, population mobility data, satellite imagery and search queries – significantly increases the accuracy of forecasts compared to traditional benchmarks. In particular, the Federal Reserve Bank of New York’s Staff Nowcast model shows that the gross domestic product forecast error is approaching the level of the first official release of the Bureau of Economic Analysis. Particular attention is paid to empirical results during the COVID-19 crisis, when models with large amounts of data reduced errors by 15–30%. The article discusses the problem statement, materials and methods, key results, as well as challenges related to data quality and privacy. The article concludes about the transformative potential of large amounts of data for economic policy and business. Practical recommendations for the implementation of real-time platforms and directions for further research, in particular the development of hybrid models and causal analysis, are proposed. The results of the study emphasize that large amounts of data are becoming a strategic tool for improving the efficiency and accuracy of economic forecasting in the face of growing uncertainty.
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