EconPapers    
Economics at your fingertips  
 

Assessing distributional properties of forecast errors for fan-chart modelling

Marian Vavra ()

Empirical Economics, 2020, vol. 59, issue 6, No 10, 2858 pages

Abstract: Abstract This paper considers the problem of assessing the distributional properties (normality and symmetry) of macroeconomic forecast errors of G7 countries for the purpose of fan-chart modelling. Our results indicate that the assumption of symmetry of the marginal distribution of forecast errors is reasonable, whereas the assumption of normality is not, making symmetric prediction intervals clearly preferable.

Keywords: Normality; Symmetry; Forecast errors; Prediction interval; Fan-chart; Sieve bootstrap (search for similar items in EconPapers)
JEL-codes: C12 C15 C22 C53 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s00181-019-01726-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: Assessing Distributional Properties of Forecast Errors (2018) Downloads
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:spr:empeco:v:59:y:2020:i:6:d:10.1007_s00181-019-01726-0

Ordering information: This journal article can be ordered from
http://www.springer. ... rics/journal/181/PS2

DOI: 10.1007/s00181-019-01726-0

Access Statistics for this article

Empirical Economics is currently edited by Robert M. Kunst, Arthur H.O. van Soest, Bertrand Candelon, Subal C. Kumbhakar and Joakim Westerlund

More articles in Empirical Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2021-10-13
Handle: RePEc:spr:empeco:v:59:y:2020:i:6:d:10.1007_s00181-019-01726-0