By Patrick Kent Watson, Sonja Sabita Teelucksingh
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Additional resources for A Practical Introduction to Econometric Methods Classical and Modern
Let us examine some of the more dire consequences of this pseudo-collinearity which, following a time-honoured tradition, we will refer to as multicollinearity. When two or more columns of X are correlated, we cannot easily distinguish the relative contributions of each variable in the explanation of y. If the column vectors comprising X are orthogonal,2 the coefficient values obtained from OLS estimation accurately reflect the degree to which each explanatory variable in the model serves to explain the variation in the variable y under study.
Let us look at the problem a little more closely. Dissatisfaction with the performance of the Cg variable may entice us to drop it from the relationship, leaving us with the model already estimated in chapter 1. 1 OLS Regression fit of Mt = β1 + β2 Yt + β3 pmt /pdt + β4 Cgt + ut ============================================================== LS // Dependent Variable is IMPORTS Date: 08/12/95 Time: 18:42 Sample: 1967 1991 Included observations: 25 ============================================================== Variable Coefficient Std.
Unfortunately, this is sometimes taken to the other extreme, and many economists are not prepared to accept any result that is at odds with their own feelings. This has led to the practice of “data mining”, in which economists fiddle with hundreds of regression runs on the computer, retaining and reporting only those that suit their own fancy. This is discussed at length in Charemza and Deadman (1997) and it is not a practice to be encouraged. 1 of the previous chapter. This represents an item of communication between the computer and the user.
A Practical Introduction to Econometric Methods Classical and Modern by Patrick Kent Watson, Sonja Sabita Teelucksingh