Home → Techniques and Tips → @RISK: General Questions → Wilkie Investment Model
All editions of @RISK can easily model time series according to the Wilkie model, using parameters that you select. With @RISK Industrial, you also have the option to fit to historical data using Time Series. The attached prototype builds two Wilkie models, Retail Price Index (RPI) and Share Dividend Yield (SY), to illustrate those techniques.
Let's start with the RPI model. Here you can either set the parameters yourself — recommended values from the literature are shown on the 'Wilkie Models' sheet — or use @RISK to estimate them using Time Series fitting with the AR1 model. @RISK lets you estimate the parameters for the price index model (mean, standard deviation, and autoregressive parameter), but in this case we fitted the transformed historical data set in column C of the 'Data' sheet and extracted those parameters from the AR1 fit; see the 'Parameters RPI' sheet. Notice that Wilkie model requires a logarithmic transformation and first order differencing detrend. Once you have found the parameters by running a fit, or picked them from the table, you can easily create the time series model with @RISK, as shown on the 'RPI' sheet.
For the SY model, we used parameters that are recommended in the literature and constructed the model directly. Please see the 'SY' sheet.
last edited: 2014-03-13