Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator

More and more are Big Data sources, such as Google Trends, being used to augment nowcast models. An often neglected issue within the previous literature, which is especially pertinent to policy environments, is the interpretability of the Big Data source included in the model. We provide a Bayesian modeling framework which is able to handle all usual econometric issues involved in combining Big Data with traditional macroeconomic time series such as mixed frequency and ragged edges, while remaining computationally simple and allowing for a high degree of interpretability. In our model, we explicitly account for the possibility that the Big Data and macroeconomic data set included have different degrees of sparsity. We test our methodology by investigating whether Google Trends in real time increase nowcast fit of US real GDP growth compared to traditional macroeconomic time series. We find that search terms improve performance of both point forecast accuracy as well as forecast density calibration not only before official information is released but also later into GDP reference quarters. Our transparent methodology shows that the increased fit stems from search terms acting as early warning signals to large turning points in GDP. KeywordsBig Data, Machine Learning, Interpretability, Illusion of Sparsity, Density Nowcast, Google Search Terms. 1We would like to thank Laurent Ferrara, Alessandro Giovonnelli, Nicolas Bonino-Gayoso, Markus Kaiser, Mark Schaffer, Gary Koop, Aubrey Poon and the conference participants of the Scottish Economic Society as well as the International Symposium on Forecasting for their invaluable comments and suggestions. 2Scottish Graduate Program in Economics and Spatial Economics & Econometrics Centre 3Spatial Economics & Econometrics Centre