ECONOMETRICS I

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ECONOMETRICS I. CHAPTER 4: CLASSICAL NORMAL LINEAR REGRESSION MODEL. Textbook: Damodar N. Gujarati (2004)  Basic Econometrics , 4th edition, The McGraw-Hill Companies. 4.1 THE PROBABILITY DISTRIBUTION OF DISTURBANCES u i.

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ECONOMETRICS I

CHAPTER 4: CLASSICAL NORMAL LINEAR REGRESSION MODEL

• Textbook: Damodar N. Gujarati (2004) Basic Econometrics, 4th edition, The McGraw-Hill Companies
4.1 THE PROBABILITY DISTRIBUTION OF DISTURBANCES ui
• The OLS estimators of and are both linear functions of ui, which is random by assumption. Therefore, the sampling or probability distributions of the OLS estimators will depend upon the assumptions made about the probability distribution of ui.
Why the normality assumtion?
• By the central limit theorem (CLT) of statistics statistics, it can be shown that if there are a large number of independent and identically distributed random variables, then, with a few exceptions, the distribution of their sum tends to a normal distribution as the number of such variables increase indefinitely. It is the CLT that provides a theoretical justification for the assumption of normality of ui .
Why the normality assumption?
• A variant of the CLT states that, even if the number of variables is not very large or if these variables are not strictly independent, their sum may still be normally distributed.
• With the normality assumption, the probability distributions of OLS estimators can be easily derived because one property of the normal distribution is that any linear function of normally distributed variables is itself normally distributed. OLS estimators and are linear functions of ui . Therefore, if ui are normally distributed, so are and , which makes our task of hypothesis testing very straightforward.
Why the normality assumtion?
• The normal distribution is a comparatively simple distribution involving only two parameters (mean and variance); it is very well known and its theoretical properties have been extensively studied in mathematical statistics. Besides, many phenomena seem to follow the normal distribution.
• Finally, if we are dealing with a small, or finite, sample size, say data of less than 100 observations, the normality assumption assumes a critical role. It not only helps us to derive the exact probability distributions of OLS estimators but also enables us to use the t, F, and χ2 statistical tests for regression models. As we will show subsequently, if the sample size is reasonably large, we may be able to relax the normality assumption.
• With the assumption that ui follow the normal distribution, the OLS estimators have the following properties: