Comparing Forecasting Performance with Panel Data

This paper develops new methods for testing equal predictive accuracy in panels of forecasts that exploit information in the time series and cross-sectional dimensions of the data. Using a common factor setup, we establish conditions on cross-sectional dependencies in forecast errors which allow us to conduct inference and compare performance on a single cross-section of forecasts. We consider both unconditional tests of equal predictive accuracy as well as tests that condition on the realization of common factors and show how to decompose forecast errors into exposures to common factors and an idiosyncratic variance component. Our tests are demonstrated in an empirical application that compares IMF forecasts of country-level real GDP growth and inflation to private-sector survey forecasts and forecasts from a simple time-series model