Hi, folks: I have a simple time series model but not estimated by arima, but I want to generate a forecast path based on historical data and estimated parameters. For example, the model is given by x=beta1*lag(x)+beta2*lag2(x); obviously the forecast for a given forecast period, say, t+k, should be xhat(t+k)=beta1*xhat(t+k-1)+beta2*xhat(t+k-2), where xhat is the forecast value. I wonder if I can use the data step to archieve this goal. The key part is to update the value of xhat iteratively. I have a data set with the structure as the following: xxxx; T X Beta1 Beta2 _________________________ 1 x (1) beta1 beta2 2 x (2) beta1 beta2 3 . beta1 beta2 4 . beta1 beta2 5 . beta1 beta2 Basically, if there is historical data, the value of x is the historical value, if not, it is missing and the value of Beta1 and Beta2 are hold at the estimated value for each observation. The task is to generate a series of Xhat. Xhat(T)=beta1*Xhat(t-1)+beta2*Xhat(t-2);, for T=3, Xhat(2)=x(2) and Xhat(1)=x(1). For T>3, we just use lag values of Xhat. Here is my code; data yyy; set xxxx; xhat=X; l1_xhat0=lag(xhat); l2_xhat0=lag(l1_xhat); xhat0=beta1*l1_xhat0+beta2*l2_xhat0; xhat=xhat0; run; However, in the dataset, yyy, the value of xhat is still missing. Can anyone give me some hints or advice on what the correct way is to do it? Thank you very much in advance. Regards, Jing
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