Real-time forecasting with macro-finance models in the presence of a zero lower bound

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Leo Krippner; Michelle Lewis

We investigate the real-time forecasting performance of macro-finance vector auto-regression models, which incorporate macroeconomic data and yield curve component estimates as would have been available at the time of each forecast, for the United States.

Our results show a clear benefit from using yield curve information when forecasting macroeconomic variables, both prior to the Global Financial Crisis and continuing into the period where the lower-bound constrained shorter-maturity interest rates. The forecasting gains, relative to traditional macroeconomic models, for inflation and the Federal Funds Rate are generally statistically significant and economically material for the horizons up to the four years that we tested. However, macro-finance models do not improve the real-time forecasts over shorter horizons for capacity utilisation, our variable representing real economic activity. This is in contrast to the related recent macro-finance literature, which establishes such results (as do we) with pseudo real-time, i.e. truncated final-vintage, data. Nevertheless, for longer horizons that are more relevant for central bankers, yield curve information does improve activity forecasts.

Overall, our results suggest that the yield curve contains fundamental information about the likely evolution of the macroeconomy. We find less convincing evidence for the reverse direction, which is likely because expectations of macroeconomic variables are already reflected in the yield curve. However, for longer horizons, we find there are still some gains from using macroeconomic variables to forecast the yield curve.