Advances in Nowcasting Economic Activity : Secular Trends, Large Shocks and New Data

The assessment of macroeconomic conditions in real time is challenging. Dynamic factor models, which summarize the comovement across many macroeconomic time series as driven by a small number of shocks, have become the workhorse tool for ‘nowcasting' activity. This paper develops a novel dynamic factor model that explicitly captures three salient features of modern business cycles: low frequency movements in long-run growth and volatility, lead-lag patterns in the responses of variables to common shocks, and fat-tailed outliers. We use real-time unrevised data for the last two decades and cloud computing technology to conduct an out-of-sample evaluation exercise of the model. The exercise demonstrates the importance of considering these features for forecasting and probability assessment of economic conditions. In an application to the COVID-19 recession, we develop a method to incorporate newly available high-frequency data. The use of such alternative data is essential to track the downturn in activity, but a careful econometric specification is just as important