This work investigates the forecasting relationship between a Google Trends indicator and real private consumption expenditure in the US. The indicator is constructed by applying Kernel Principal Component Analysis to consumption-related Google Trends search categories. The predictive performance of the indicator is evaluated in relation to two conventional survey-based indicators: the Conference Board Consumer Confidence Index and the University of Michigan Consumer Sentiment Index. The findings suggest that in both in-sample and out-of-sample nowcasting estimations the Google indicator performs better than survey-based predictors. The results also demonstrate that the predictive performance of survey-augmented models is no different than the power of a baseline autoregressive model that includes macroeconomic variables as controls. The results demonstrate an enormous potential of Google Trends data as a tool of unmatched value to forecasters of private consumption.
