Specification Error When constructing any regression model, we are always most interested in explaining what variables cause the dependent variable to change and by how much. This will always depend on a combination of economic theory; basic human behavior; and past experience. One of the assumptions of OLS is that the model is correctly specified. The specification error can be explained by these two aspects : - a) Missing / omitting relevant information / explanatory variables or from including irrelevant variables. b) Incorrect functional form. This lecture will discuss the following issues : which regressors should be included and / or excluded from a particular model. In other words, we will consider the following cases : - a) A regression model that excludes some important explanatory variables. b) A regression model that includes some irrelevant regressors. 1) Exclusion of relevant variables Suppose that we are interested in the following model : - The question is whether the set of L regressors - - are important variables that should be included in the model. But because of a certain reason, we have to use the following model : - For illustration, we can use a model with only two explanatory variables. The model with two explanatory variables is specified as follows : - True model