Improving the Reliability of Real-time Hodrick-Prescott Filtering Using Survey Forecasts

Measuring economic activity in real-time is a crucial issue in applied research and in the decision-making process of policy makers; however, it also poses intricate challenges to statistical filtering methods that are built to operate optimally under the auspices of an infinite number of observations. In this paper, we propose and evaluate the use of survey forecasts to augment one of those methods, namely the largely used Hodrick-Prescott filter so as to attenuate the end-of-sample uncertainty observed in the resulting gap estimates. We find that this approach achieves powerful improvements to the real-time reliability of these economic activity measures, and we argue that the use of surveys is preferable relative to model-based forecasts due to both an usually superior accuracy in predicting current and future states of the economy and its parsimony.