A lot, including a few things you may not expect. Previous studies find that the term spread forecasts GDP but these regressions are unconstrained and do not model regressor endogeneity. We build a dynamic model for GDP growth and yields that completely characterizes expectations of GDP. The model does not permit arbitrage. Contrary to previous findings, we predict that the short rate has more predictive power than any term spread. We confirm this finding by forecasting GDP out-ofsample. The model also recommends the use of lagged GDP and the longest maturity yield to measure slope. Greater efficiency enables the yield-curve model to produce superior out-of-sample GDP forecasts than unconstrained OLS regressions at all horizons.

Surveys do! We examine the forecasting power of four alternative methods of forecasting U.S. inflation out-of-sample: time series ARIMA models; regressions using real activity measures motivated from the Phillips curve; term structure models that include linear, non-linear, and arbitrage-free specifications; and survey-based measures. We also investigate several optimal methods of combining forecasts. Our results show that surveys outperform the other forecasting methods and that the term structure specifications perform relatively poorly. We find little evidence that combining forecasts using means or medians, or using optimal weights with prior information produces superior forecasts to survey information alone. When combining forecasts, the data consistently places the highest weights on survey information.

Highlights d Cryo-EM structure of SARS-CoV-2 nsp12-nsp7-nsp8 core polymerase complex d The core complex of SARS-CoV-2 has lower enzymatic activity than SARS-CoV d SARS-CoV-2 nsp7-8-12 subunits are less thermostable than the SARS-CoV counterpart

Surveys do! We examine the forecasting power of four alternative methods of forecasting U.S. inflation out-of-sample: time series ARIMA models; regressions using real activity measures motivated from the Phillips curve; term structure models that include linear, non-linear, and arbitrage-free specifications; and survey-based measures. We also investigate several optimal methods of combining forecasts. Our results show that surveys outperform the other forecasting methods and that the term structure specifications perform relatively poorly. We find little evidence that combining forecasts using means or medians, or using optimal weights with prior information produces superior forecasts to survey information alone. When combining forecasts, the data consistently places the highest weights on survey information.