Analysis Summary

Data variable: AUTOADJ/CPI

Number of observations = 314
Start index = 1/70            
Sampling interval = 1.0 month(s)
Length of seasonality = 12

Forecast Summary
----------------
Forecast model selected: Constant mean = 1.40164 + 1 regressor
Number of forecasts generated: 0
Number of periods withheld for validation: 26

            Estimation      Validation
Statistic   Period          Period
--------------------------------------------
MSE         4.10166         10.6607         
MAE         1.64644         3.05438         
MAPE        8.3251          9.9606          
ME          -1.43836E-14    3.05438         
MPE         -1.041          9.9606          

                            Trend Model Summary
Parameter           Estimate        Stnd. Error     t               P-value
----------------------------------------------------------------------------
Constant            1.40164         0.709646        1.97513         0.049215
INCOME/CPI          0.0782192       0.0028257       27.6814         0.000000
----------------------------------------------------------------------------


The StatAdvisor
---------------
   This procedure will forecast future values of AUTOADJ/CPI.  The
data cover 314 time periods.  Currently, a mean model has been
selected.  This model assumes that the best forecast for future data
is given by the average of all previous data.  You can select a
different forecasting model by pressing the alternate mouse button and
selecting Analysis Options.

   The output summarizes the statistical significance of the terms in
the forecasting model.  Terms with P-values less than 0.05 are
statistically significantly different from zero at the 95% confidence
level.  In this case, the P-value for the mean is less than 0.05, so
it is significantly different from 0.0.  The model also includes one
independent regression variable.  Since INCOME/CPI has a P-value which
is less than 0.05, it is statistically significant at the 95%
confidence level.  

   The table also summarizes the performance of the currently selected
model in fitting the previous data.  It displays: 
   (1) the mean squared error (MSE)
   (2) the mean absolute error (MAE)
   (3) the mean absolute percentage error (MAPE)
   (4) the mean error (ME)
   (5) the mean percentage error (MPE)
Each of the statistics is based on the one-ahead forecast errors,
which are the differences between the data value at time t and the
forecast of that value made at time t-1.  The first three statistics
measure the magnitude of the errors.  A better model will give a
smaller value.  The last two statistics measure bias.  A better model
will give a value close to 0.0.  In this case, the model was estimated
from the first 288 data values.  26 data values at the end of the time
series were withheld to validate the model.  The table shows the error
statistics for both the estimation and validation periods.  If the
results are considerably worse in the validation period, it means that
the model is not likely to perform as well as otherwise expected in
forecasting the future.