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时间序列自激门限自回归型非线性的综合检验AI 解读

A portmanteau test to detect self-exciting threshold autoregressive-type nonlinearity in time series data is proposed. The test is based on cumulative sums of standardized residuals from autoregressive fits to the data. Significance levels for the test under the hypothesis of linearity are obtain from the asymptotic distribution of the cumulative sums as Brownian motion. The performance of the test is evaluated for simulated data from linear, bilinear and self-exciting…

广义偏自相关函数用于ARMA过程定阶的局限AI 解读

We show that the General Partial Autocorrelation Function (GPAC), which has recently been suggested to be used as one of a set of convenient tools for order identification in ARMA models, has unstable behavior when applied to time series of moderate length. Its use in detecting the order of MA components in real series is very limited and can only be recommended as a means to confirm a pure AR fit to the data.

时间序列模型设定的综合检验功效研究AI 解读

In this note we present simulation evidence on the power of a portmanteau statistic used to detect time series model misspecification. This is related to the loss in forecasting accuracy resulting from use of the incorrectly specified model. Our conclusion is that the statistic achieves a high level of success only when sample size is large.

时间序列模型设定综合检验的若干功效研究AI 解读

In this note we present simulation evidence on the power of a portmanteau statistic used to detect time series model misspecification. This is related to the loss in forecasting accuracy resulting from use of the incorrectly specified model. Our conclusion is that the statistic achieves a high level of success only when sample size is large.