Improving the reliability of real-time output gap estimates using survey forecasts
Jaqueson Galimberti () and
Marcelo Moura ()
International Journal of Forecasting, 2016, vol. 32, issue 2, 358-373
Measuring economic activity in real-time is a crucial issue both 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 when working with an infinite number of observations. In this paper, we propose and evaluate the use of survey forecasts for augmenting such methods, in order to reduce the end-of-sample uncertainty that is observed in the resulting gap estimates. We focus on three filtering methods that are employed commonly in business cycle research: the Hodrick-Prescott filter, unobserved components models, and the band-pass filter. We find that the use of surveys achieves powerful improvements in the real-time reliability of the economic activity measures associated with these filters, and argue that this approach is preferable to model-based forecasts due to both its usually superior accuracy in predicting current and future states of the economy and its parsimony.
Keywords: Business cycles measurement; End-of-sample uncertainty; Gap and trend decomposition (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:32:y:2016:i:2:p:358-373
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 Nithya Sathishkumar ().