e=arima.sim(n=1000,list(order=c(0,0,0))) e1=lag(e,1) e2=lag(e,2)

These lines of code simulate a time series of 1000 observations using the ARIMA model with order (0,0,0), which is equivalent to a white noise process. The second and third lines create two new series, e1 and e2, which are the original series shifted by one and two time periods, respectively. These lagged series can be used as input variables in a regression model to capture the time dependence structure in the original series.

相关问题

e=arima.sim(n=1000,list(order=c(0,0,0))) e1=lag(e,1) e2=lag(e,2) x1=e-2*e1 x2=e-0.5*e1 x3=e-4/5*e1+16/25*e2 x4=e-5/4*e1+25/16*e2

These are four different time series models that each include a white noise error term e with different lagged terms:

- Model x1 includes a lagged error term e1 with coefficient -2.
- Model x2 includes a lagged error term e1 with coefficient -0.5.
- Model x3 includes lagged error terms e1 with coefficient -4/5 and e2 with coefficient 16/25.
- Model x4 includes lagged error terms e1 with coefficient -5/4 and e2 with coefficient 25/16.

Each of these models is an ARIMA model with a differencing order of 0 and no autoregressive or moving average terms. The lagged error terms are included as exogenous variables in the model to capture any additional dependence on past values of the error term beyond the white noise assumption. The specific lagged terms and coefficients are chosen based on some underlying theory or hypothesis about the data generating process.

arima.sim(n = 1000, list(ar = c(-0.8, 0.5), ma = -1.1)) : 模型的'ar'部分不平穩

翻译：arima.sim(n = 1000, list(ar = c(-0.8, 0.5), ma = -1.1)) ：模型的'ar'部分不平稳。

该函数尝试使用 ARIMA 模型来模拟时间序列数据，但是由于 AR 部分不平稳，因此模拟失败。这可能是由于 AR 部分包含非平稳性或趋势导致的。需要重新考虑模型或先对数据进行适当的差分或转换。