Interpretation and use of sensitivity in econometrics, illustrated with forecast combinations

Jan Magnus () and Andrey Vasnev

International Journal of Forecasting, 2015, vol. 31, issue 3, 769-781

Abstract: Sensitivity analysis is important both for its own sake and in combination with diagnostic testing. We consider the question of how to use sensitivity statistics in practice, and in particular, how to judge whether the sensitivity is large or small. For this purpose, we distinguish between absolute and relative sensitivity, and highlight the context-dependent nature of sensitivity analysis. The relative sensitivity is then applied to forecast combinations, and sensitivity-based weights are introduced. All of the concepts are illustrated using the European yield curve. In this context, it is natural to consider the sensitivity to autocorrelation and normality assumptions. Different forecasting models are combined using equal, fit-based and sensitivity-based weights, and compared with the multivariate and random walk benchmarks. We show that the fit-based and sensitivity-based weights are complementary, but that the sensitivity-based weights perform better than other weights for long-term maturities.

Keywords: Sensitivity analysis; Reconciling forecasts; Yield curve prediction (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207013001325
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:31:y:2015:i:3:p:769-781

DOI: 10.1016/j.ijforecast.2013.08.001

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 ().