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By Gujarati, Damodar N

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Stated otherwise, why not develop a multiple regression model with as many variables as possible? The reasons are many. 1. Vagueness of theory: The theory, if any, determining the behavior of Y may be, and often is, incomplete. We might know for certain that weekly income X influences weekly consumption expenditure Y, but we might be ignorant or unsure about the other variables affecting Y. Therefore, ui may be used as a substitute for all the excluded or omitted variables from the model. 2. Unavailability of data: Even if we know what some of the excluded variables are and therefore consider a multiple regression rather than a simple regression, we may not have quantitative information about these 8 See App.

But the reader can readily see that such over- and underestimation is inevitable because of sampling fluctuations. The critical question now is: Granted that the SRF is but an approximation of the PRF, can we devise a rule or a method that will make this approximation as “close” as possible?

Gujarati: Basic Econometrics, Fourth Edition I. Single−Equation Regression Models 2. Two−Variable Regression Analysis: Some Basic Ideas CHAPTER TWO: © The McGraw−Hill Companies, 2004 TWO-VARIABLE REGRESSION ANALYSIS: SOME BASIC IDEAS 47 our theory is not strong enough to suggest what other variables might be included, why introduce more variables? Let ui represent all other variables. Of course, we should not exclude relevant and important variables just to keep the regression model simple. 7. Wrong functional form: Even if we have theoretically correct variables explaining a phenomenon and even if we can obtain data on these variables, very often we do not know the form of the functional relationship between the regressand and the regressors.

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