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

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. ; We thank the Spanish Government, research projects ECO2015–237033–C2–2–R and ECO2015–65701–P(MINECO/FEDER), for financial suppor