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ARIMA 模型识别中不同定阶准则的比较
The small-sample performance of several order-determination criteria for identification of ARIMA models is compared using simulated and economic data. The authors also demonstrate how the residual white-noise autoregressive order-determination criterion can be used to identify unit roots in nonstationary data.
ARMA 模型的阶数识别
We present a new powerful method for determining the order of ARMA (p, q) models having small sets of observations. The procedure is based on an autoregressive order determination criterion and on linear estimation methods. Simulated data are used to demonstrate the capabilities of the approach.
ARMA模型的阶数识别
We present a new powerful method for determining the order of ARMA (p, q) models having small sets of observations. The procedure is based on an autoregressive order determination criterion and on linear estimation methods. Simulated data are used to demonstrate the capabilities of the approach.
平稳单变量时间序列新息方差的频域估计
The innovation variance σ2 of a linear stochastic time series model can be estimated using periodogram ordinates. However, since the periodogram ordinates as estimators of the corresponding spectrum ordinates can show appreciable small-sample bias, it is believed that the estimator of σ2 is biased. One way of reducing the small-sample bias in the periodogram ordinates is by tapering. In this paper an estimator of σ2 based on tapered time series is defined and evaluated…