Predicting Food Prices using Data from Consumer Surveys and Search

Predicting future food prices is important not only for projecting and adjusting the cost of government programs but also for business and household planning. This study asks whether unconventional consumer-oriented measures might be useful in the predicting Bureau of Labor Statistics (BLS) Food and Beverages Consumer Price Index (CPI). We investigate the ability of Internet search-based index related to food prices (the Google trends index) and survey-based sentiment indices (the index of consumer sentiment) to predict changes in food-related BLS prices from January 2004 to July 2015. We consider several forecasting models and find that a vector autoregression model (VAR) results in the lowest root mean square error and mean absolute percentage error. We also ask whether our model can out predict USDA Economic Research Service food-related CPI forecasts. Rolling window comparison and encompassing tests are conducted, and we find that our new model including consumer-oriented measures outperforms the USDA model in terms of predictive accuracy.