UNCERTAINTY AND DENSITY FORECASTS OF ARMA MODELS: COMPARISON OF ASYMPTOTIC, BAYESIAN, AND BOOTSTRAP PROCEDURES
研究了参数估计、误差分布和滞后阶数三种不确定性对平稳线性ARMA模型密度预测的影响，比较了渐近、贝叶斯和自助法在小样本下的表现，并用英国通胀数据绘制扇形图说明。
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.