ABSTRACT
In the previous two chapters we discussed the regression methodologies and how they can be applied to business and economic forecasting. In this chapter we expand on the basic model and explore several concepts that are very helpful when dealing with regression models for forecasting. As has been discussed throughout this text, the aim of the forecaster is to minimize the level of error and produce a forecast that guides management in their decisions. The premise on which the discussions of the simple and multiple regression were based is the assumption that the analyst has properly identified and included those variables of interest that appropriately explain what happens to the dependent variable. The techniques that are discussed in this chapter provide further refinement in identifying the independent variables and developing a regression model that closely captures the nuances between the variables of interest. In the next section we will discuss how proxy and dummy variables are used to develop a regression model that closely approximates a business or economic condition.
