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Basic Econometrics

Basic Econometrics. Chapter 4 : THE NORMALITY ASSUMPTION: Classical Normal Linear Regression Model (CNLRM). 4-2.The normality assumption. CNLR assumes that each u i is distributed normally u i  N(0,  2 ) with: Mean = E(u i ) = 0 Ass 3

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Basic Econometrics

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  1. Basic Econometrics Chapter 4: THE NORMALITY ASSUMPTION: Classical Normal Linear Regression Model (CNLRM) Prof. Himayatullah

  2. 4-2.The normality assumption • CNLR assumes that each u i is distributed normally u i  N(0, 2) with: Mean = E(u i) = 0 Ass 3 Variance = E(u2i) = 2 Ass 4 Cov(u i , u j ) = E(u i , u j) = 0 (i#j) Ass 5 • Note: For two normally distributed variables, the zero covariance or correlation means independence of them, so u i and u j are not only uncorrelated but also independently distributed. Therefore u i  NID(0, 2) is Normal and Independently Distributed Prof. Himayatullah

  3. 4-2.The normality assumption • Why the normality assumption? • With a few exceptions, the distribution of sum of a large number of independent and identically distributed random variables tends to a normal distribution as the number of such variables increases indefinitely • If the number of variables is not very large or they are not strictly independent, their sum may still be normally distributed Prof. Himayatullah

  4. 4-2.The normality assumption • Why the normality assumption? • Under the normality assumption for ui , the OLS estimators ^1 and ^2 are also normally distributed • The normal distribution is a comparatively simple distribution involving only two parameters (mean and variance) Prof. Himayatullah

  5. 4-3. Properties of OLS estimators under the normality assumption • With the normality assumption the OLS estimators ^1 , ^2 and ^2 have the following properties: 1. They are unbiased 2. They have minimum variance. Combined 1 and 2, they are efficient estimators 3. Consistency, that is, as the sample size increases indefinitely, the estimators converge to their true population values Prof. Himayatullah

  6. 4-3. Properties of OLS estimators under the normality assumption 4. ^1 is normally distributed  N(1, ^12) And Z = (^1- 1)/ ^1 is  N(0,1) 5. ^2 is normally distributed N(2 ,^22) And Z = (^2- 2)/ ^2 is  N(0,1) 6. (n-2) ^2/ 2 is distributed as the 2(n-2) Prof. Himayatullah

  7. 4-3. Properties of OLS estimators under the normality assumption 7. ^1 and ^2 are distributed independently of ^2. They have minimum variance in the entire class of unbiased estimators, whether linear or not. They are best unbiased estimators (BUE) 8. Let uiis  N(0, 2 ) then Yi is  N[E(Yi); Var(Yi)] = N[1+ 2X i ; 2] Prof. Himayatullah

  8. Some last points of chapter 4 4-4. The method of Maximum likelihood (ML) • ML is point estimation method with some stronger theoretical properties than OLS (Appendix 4.A on pages 110-114) The estimators of coefficients ’s by OLS and ML are • identical. They are true estimators of the ’s • (ML estimator of 2) = u^i2/n (is biased estimator) • (OLS estimator of 2) = u^i2/n-2 (is unbiased estimator) • When sample size (n) gets larger the two estimators tend to be equal Prof. Himayatullah

  9. Some last points of chapter 4 4-5. Probability distributions related to the Normal Distribution: The t, 2, and F distributions See section (4.5) on pages 107-108 with 8 theorems and Appendix A, on pages 755-776 4-6. Summary and Conclusions See 10 conclusions on pages 109-110 Prof. Himayatullah

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