Least absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity
条件异方差分数单整 ARMA 模型的最小绝对偏差估计
We consider a unified least absolute deviation estimator for stationary and nonstationary fractionally integrated autoregressive moving average models with conditional heteroscedasticity. Its asymptotic normality is established when the second moments of errors and innovations are finite. Several other alternative estimators are also discussed and are shown to be less efficient and less robust than the proposed approach. A diagnostic tool, consisting of two portmanteau…