This article addresses one of the drawbacks of survey-based measures of expectations, the fact that updates are relatively infrequent, due to the monthly or quarterly frequency of survey waves. To obtain a more frequent measure, I present a state space method that uses time-stamped survey responses as its daily measurements and the population distribution of expectations as the latent state. Augmenting the method with financial variables that measure expectations indirectly (e.g., bond yields) facilitates measurement at times when no responses are observed. An application to a survey of German financial experts shows that my method is successful for forecasting future responses. Additional analyses show that its daily estimates react in economically plausible ways to both major events, such as the September 2008 crash of Lehman Brothers, and regular releases of economic indicators.

Mokinski, Frieder (2016), Using time-stamped survey responses to measure expectations at a daily frequency, International Journal of Forecasting Volume 32, Issue 2, 271-282. Download


Surveys; State space models; Kalman filter; Time series; Disaggregation