Uncertainty and density forecasts of ARMA models: comparison of asymptotic, bayesian, and bootstrap procedures Articles uri icon

publication date

  • April 2018

start page

  • 388

end page

  • 419

issue

  • 2

volume

  • 32

international standard serial number (ISSN)

  • 0950-0804

electronic international standard serial number (EISSN)

  • 1467-6419

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

keywords

  • Monetary policy
    Decision making
    Mathematical models of economics
    Bayesian analysis
    Bootstrapping (Statistics)
    Bayesian forecast
    Bootstrap
    Fan charts
    Model misspecification
    Parameter uncertainty