## 这是一篇来自美国Spring Semester 2023的关于编写的SAS程序的副本，输出和任何相关图表，当然还有您对问题的分析和解决方案的程序代写

These problems are due by **the beginning of class, Wednesday, March 29, 2023**. No exceptions! You should submit a copy of the SAS program **you **wrote in order to solve the problem, the output and any relevant charts, and of course **your **analysis of and solution to the problem.

Please follow the specifications for submitting the problems (posted separately). Failure to do so will result in a 10 percent penalty on the problem set. I reserve the right to *escalate *the penalty on future problem sets if you insist on failing to adhere to the specifications.

late problem sets will not be accepted!

**three**real-world time series data sets. The last worksheet of the workbook has a short data dictionary.

A.Use SAS to plot each original data series *before *proceeding to the next phase of analysis. State your impressions of each series **before **doing any identification or modeling attempts. This *may include *whether or not to apply a **pre-differencing transformation. **

B.Use SAS to obtain the SACF and SPACF for each series and tentatively identify **a ****time series model **for each series. You must decide on **one model **— you *may not *hedge your bets! If you insist on selecting multiple models, I will take the first identification you make as your solution and will **ignore **the rest.

**C.Detach the last 10 percent of the observations for each series. **Use SAS to estimate the model *that you identified *using the “trimmed” data set. **Discuss **(based on the SAS output) how well you were able to identify the model.

D.Use your model to **forecast **the observations you deleted from the full data set. (Of course, the number of forecasts should match the number of observations in your holdout set.) Compute the RMSE for the forecast.

E.Use one of the *naive forecasting methods *to compute a pseudo out-of-sample forecast and find the RMSE for the naive forecast. **Explain why **you selected your naive model.

Compare the RMSE from the naive forecast with that from the Box-Jenkins model. Which forecasting method worked better? Note: Of course, you are doing a naive forecast for **each series. **

F.Finally, plot the actuals, fits, confidence band and forecast for each ARIMA forecast.

Do **not **include the naive forecast in the plot.