Publication:
Uncertainty and density forecasts of ARMA models: comparison of asymptotic, bayesian and bootstrap procedures

Thumbnail Image
Identifiers
URI: https://hdl.handle.net/10016/34948
ISSN: 0950-0804
DOI: https://doi.org/10.1111/joes.12197
UXXI: AR/0000023231
Files
uncertainty_JES_2018_ps.pdf (621.82 KB)
Publication date
2018-04-01
Defense date
Authors
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley
Impact
Google Scholar
Export
Research Projects
Organizational Units
Journal Issue
Abstract
The objective of this paper is to analyze the effects of uncertainty on density forecasts of stationary linear univariate ARMA models. We consider three specific sources of uncertainty: parameter estimation, error distribution, and lag order. Depending on the estimation sample size and the forecast horizon, each of these sources may have different effects. We consider asymptotic, Bayesian, and bootstrap procedures proposed to deal with uncertainty and compare their finite sample properties. The results are illustrated constructing fan charts for UK inflation.
Description
Keywords
Bayesian forecast, Bootstrap, Fan charts, Parameter uncertainty, Model misspecification
Bibliographic citation
Gonçalves Mazzeu, J.H., Ruiz, E., & Veiga, H. (2018).Uncertainty and density forecasts of arma models: comparison os asymptotic, bayesian, and bootstrap procedures. Journal of Economic Surveys, 32 (2), pp. 388–419.
Collections
DES - Artículos de Revistas