Improving the mechanisms for forecasting inflation is an important part of economic science. National central banks, which monitor and manage the dynamics of the price level in the economy, use and develop these mechanisms in practice. Scientists and bank analysts have developed an impressive variety of ways to obtain estimates of inflation expectations of professional economists and ordinary citizens, as well as models for predicting future inflation values. In the last ten years, big data obtained from the Internet has been increasingly used for nowcasting inflation expectations and forecasting price dynamics. In this article, using the methods of correlation and regression analysis, it is demonstrated the validity of measuring the inflation expectations based on queries in Google Trends. In addition, these data turned out to be a fairly good predictor of the CPI level with a one-month lag. And combining the traditional CPI with a one-month lag and query statistics gave the lowest forecast error of all the model specifications considered. The resulting model is more flexible than the classical methods of forecasting inflation, including by taking into account the psychological aspects of economic behavior.
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