Improving the reliability of real-time Hodrick-Prescott filtering using survey forecasts

Incorporating survey forecasts to a forecast-augmented Hodrick-Prescott filter, we evidence a considerable improvement to the reliability of US output-gap estimation in realtime. Odds of extracting wrong signals of output-gap estimates are found to reduce by almost a half, and the magnitude of revisions to these estimates accounts to only three fifths of the output-gap average size, usually an one-by-one ratio. We further analyze how this end-of-sample uncertainty evolves as time goes on and observations accumulate, showing that a 90% rate of correct assessments of the output-gap sign can be attained with five quarters of delay using survey forecasts.