Using time-stamped survey responses to measure expectations at a daily frequency

Frieder Mokinski

International Journal of Forecasting, 2016, vol. 32, issue 2, 271-282

Abstract: 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.

Keywords: Surveys; State space models; Kalman filter; Time series; Disaggregation (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207015001053
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:32:y:2016:i:2:p:271-282

DOI: 10.1016/j.ijforecast.2015.06.004

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().