Finite Sample Comparison of Parametric, Semiparametric, and Wavelet Estimators of Fractional Integration
通过蒙特卡洛模拟比较分数差分参数d的多种估计量（频域、时域、小波）在有限样本下的偏差和均方根误差，发现频域极大似然法优于时域参数法，局部多项式Whittle和偏差缩减对数周期图回归对短期动态更稳健，而小波估计需充分修剪尺度。
In this paper we compare through Monte Carlo simulations the finite sample properties of estimators of the fractional differencing parameter, d. This involves frequency domain, time domain, and wavelet based approaches and we consider both parametric and semiparametric estimation methods. The estimators are briefly introduced and compared, and the criteria adopted for measuring finite sample performance are bias and root mean squared error. Most importantly, the simulations reveal that 1) the frequency domain maximum likelihood procedure is superior to the time domain parametric methods, 2) all the estimators are fairly robust to conditionally heteroscedastic errors, 3) the local polynomial Whittle and bias reduced log-periodogram regression estimators are shown to be more robust to short-run dynamics than other semiparametric (frequency domain and wavelet) estimators and in some cases even outperform the time domain parametric methods, and 4) without sufficient trimming of scales the wavelet based estimators are heavily biased.