Research Writing
€30-250 EUR
Paid on delivery
The Excel workbook attached herewith, contains two time series; both are ac- tual (not simulated) data. Each tab contains one series. A kind of “data dictionary” is contained within each worksheet.
A. Plot each time series and write down initial impressions about the series (does it appear to contain a trend, does there appear to be seasonality, etc.). If you decide to use a variance-stabilizing transformation I do not need to see the plot of the transfomed series. But you should state explicitly that you are applying one.
B. Use SAS to obtain the ACF and PACF for each series. Identify the best likely time series model for each series. Be sure to explain your reasoning!
C. Detach the last 10 percent of observations from each series to use as a “holdout” set. Estimate your model for each series using only the retained data. Evaluate each model in terms of its in-sample fit. Also do pseudo out-of-sample forecasts for the “missing” observations.
D. One tool to evaluate forecasts is to compute the root mean square error, or RMSE. Use SAS to implement this formula for forecasts of your holdout sets. Evaluate each model in terms of the forecast performance versus the holdout set.
E. Now find an alternative model for each series and use those to generate pseudo out-of-sample forecasts. Compute the RMSE for the forecasts from the alternative models. Evaluate the alternative models in terms of in-sample fit and forecasting performance. Make
Project ID: #14025137
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