Solved – SAS’ PROC ARIMA does not converge

arimasas

I have an error that says

The estimation algorithm did not converge after 50 iterations and the forecast was not done.

What can I do to prevent this error? Like how can I check the data before forecasting?

Best Answer

In general, if an ARIMA estimation does not converge, it is likely that the model was not a good fit for the data. However, you can increase the number of iterations by adding the argument MAXITER=200, for example.

Related Solutions

Solved – Time series modeling with dynamic regressors in SAS vs R

While it is not built in, it is easy enough to do:

library(forecast)
fity <- auto.arima(y, xreg=x)
fitx <- auto.arima(x)
forecast(fity,h=10,xreg=forecast(fitx,h=10)$mean)

However, the prediction intervals obtained do not account for the variance in the forecasts of x. I doubt that SAS handles this either.

Solved – Get fitted values estimated in ARIMA in Matlab

I believe what you want can be indirectly obtained using the function infer.

As in general the actual fitted values from an ARIMA model are of little use themselves, what MATLAB returns is the residuals vector (somewhat oddly). In your case after you have estimated the parameters of the ARIMA model of your choice (model) given your observed univariate time series (data), you can use the model containing the parameter estimates (est) to infer the residuals of your model. So you want something like:

residuals = infer(est,data);

Following that you actual estimated values (est_data) are simply:

est_data = data - residuals;

Take note that infer function uses default setting regarding presample data. I do not know what are R is doing to get its estimates so I would advice you to look at the notion of presample before continuing. You might be surprised about what assumptions are put in place by different packages.

Best Answer

Related Solutions

Solved – Time series modeling with dynamic regressors in SAS vs R

Solved – Get fitted values estimated in ARIMA in Matlab

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