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It may sometimes be the case that the economic theory under study may itself be defective, causing the 48 A Practical Introduction to Econometric Methods: Classical and Modern investigator to misspecify the model. Or, as is so frequent in the field of economics, the theory may involve variables that are not directly measurable, or for which data are just not readily available, such as, for example, consumer tastes in a demand function, or capital stock in a production function. Whatever the reason for the misspecification of a model, its effect is to subsume the influence of the missing variable(s) into the disturbance term, and it can (and will) be shown that, in this case, OLS estimation is biased.

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. Error T-Statistic Prob.

1n X2 σ21 σ22 ........... σ2n Xn σn1 σn2 ......... σn2 The tableau clearly shows, in an ordered way, the variances (along the principal diagonal) and the covariances (the nondiagonal elements). This tableau is defined as 9 Strictly speaking, we should talk about the mean and variance of a distribution of a random variable, and not of the random variable itself. 22 A Practical Introduction to Econometric Methods: Classical and Modern the variance-covariance matrix and sometimes more simply as the covariance or dispersion matrix of the random vector X.

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