NONSTATIONARITY-EXTENDED WHITTLE ESTIMATION
研究了长记忆时间序列模型中Whittle估计的渐近正态性，提出非平稳性扩展的Whittle估计方法，适用于分数自回归移动平均模型与GARCH型误差，估计量比锥化Whittle估计更有效。
For long memory time series models with uncorrelated but dependent errors, we establish the asymptotic normality of the Whittle estimator under mild conditions. Our framework includes the widely used fractional autoregressive integrated moving average models with generalized autoregressive conditional heteroskedastic-type innovations. To cover nonstationary fractionally integrated processes, we extend the idea of Abadir, Distaso, and Giraitis (2007, Journal of Econometrics 141, 1353–1384) and develop the nonstationarity-extended Whittle estimation. The resulting estimator is shown to be asymptotically normal and is more efficient than the tapered Whittle estimator. Finally, the results from a small simulation study are presented to corroborate our theoretical findings.