Under-forecasting in Regression I have to do forecasting of sales that is how much sales of a product is going to happen in a particular store.
I have time series data for last two years and doing forecasting for 2014.
The variables are promotion flag ( Yes/ No ), promotion period, location in a store, price discount.
These all are categorical variables.
For this I am using regression method where, dependent variable is sales, and independent variables are categorical variables mentioned above. This analysis is done in SPSS where I have used step-wise and backward regression. 
Below is the link for the data:
https://drive.google.com/file/d/0B7GTV25JHGcDWnQ1a280SmtzUUU/edit?usp=sharing
I want to know, the regression model is under-forecasting? Is there a way to improve the forecast?
 A: You should investigate adding the impact of the day-of-the-week, weekly indicators,monthly indicators and the effect of holidays. Holiday/events routinely have lead and lag effects. Furthermore there may be level shifts in your data or local time trends . Additionally there may be an ARIMA component that needs to be included. Please post your data for one of the stores and I will be more specific.
You might want to look at
Wrong predictions for weekend, but good predictions for weekdays
as it discusses your problem/opportunity
A: Its important to define the problem correctly and then you can model it. I am bit confused reading the problem.
If you are trying to predict value of Response variable SALES using Prediction variables like Price, Promo and / or Ad Placement then use Multiple Regression Model. Then you can try to fit it.
On other-hand if you are developing a Forecasting Model for SALES for future then use Exp (Double) Smoothing, Winter Method and Arima model etc.
If you are doing later and the results are not good then use esemble methods.
