Once we have built the regression model, we need to generate scores for a holdout sample. The holdout sample contains one month of observed data. At times, when building the model, we over fit the data. It is always a good idea to have a holdout sample on which the model can be fitted. There isn't anything better than using the observed values to see how well the predictions were made. The holdout sample shouldn't be too short or too future looking when compared to what has been built into the model.

We cannot expect the model that we have built for daily stock prices using almost three years of data to help us predict three years ahead. Also, using our model to predict just one day ahead and using that as a holdout sample would be too lenient on the model, and would not check for its practical use with a decent data size.

We will write the model parameters to the REGOUT dataset. We have stored our holdout data in a table called predict. The regression equation will be used to generate forecasts on this dataset.

The code to generate a PROC REG based forecast is as follows:

PROC REG DATA=build OUTEST=REGOUT; 
ID DATE; 
MODEL Stock = basket_index eps p_e_ratio global_mkt_share media_analytics_index 
m1_money_supply_index top_10_gdp; 
RUN; 
 
PROC SCORE DATA=validation 
SCORE=REGOUT OUT=RSCOREP TYPE=PARMS; 
var basket_index eps p_e_ratio global_mkt_share media_analytics_index 
m1_money_supply_index top_10_gdp; 
RUN; 

Before we discuss the results of the preceding code, let us explore ARIMA in some detail.