1. Classical Linear Regression Model Normality Assumption Hypothesis Testing Under Normality Maximum Likelihood Estimator Generalized Least Squares

2. Normality Assumption Assumption 5  |X ~ N(0,  2 I n ) Implications of Normality Assumption –(b-  )|X ~ N(0,  2 (X′X) -1 ) –(b k -  k )|X ~ N(0,  2 ([X′X) -1 ] kk )

3. Hypothesis Testing under Normality Implications of Normality Assumption –Because  i /  ~ N(0,1), where M = I-X(X′X) -1 X′ and trace(M) = n-K.

4. Hypothesis Testing under Normality If  2 is not known, replace it with s 2. The standard error of the OLS estimator  k is SE(b k ) = Suppose A.1-5 hold. Under H 0 : the t-statistic defined as ~ t(n-K).

5. Hypothesis Testing under Normality Proof of ~ t(n-K)

6. Hypothesis Testing under Normality Testing Hypothesis about Individual Regression Coefficient, H 0 : –If  2 is known, use z k ~ N(0,1). –If  2 is not known, use t k ~ t(n-K). Given a level of significance , Prob(-t  /2 (n-K) < t < t  /2 (n-K)) = 1- 

7. Hypothesis Testing under Normality –Confidence Interval [b k -SE(b k ) t  /2 (n-K), b k +SE(b k ) t  /2 (n-K)] –p-Value: p = Prob(t>|t k |) x 2 Prob(-|t k | |t k |) = Prob(t . Reject otherwise.

8. Hypothesis Testing under Normality Linear Hypotheses H 0 : R  = q

9. Hypothesis Testing under Normality Let m = Rb-q, where b is the unrestricted least squares estimator of . –E(m|X) = E(Rb-q|X) = R  -q = 0 –Var(m|X) = Var(Rb-q|X) = RVar(b|X)R′ =  2 R(X′X) -1 R′ Wald Principle W = m′Var(m|X) -1 m = (Rb-q)′[  2 R(X′X) -1 R′] -1 (Rb-q) ~ χ 2 (J), where J is the number of restrictions Define F = (W/J)/(s 2 /  2 ) = (Rb-q)′[s 2 R(X′X) -1 R′] -1 (Rb-q)/J

10. Hypothesis Testing under Normality Suppose A.1-5 holds. Under H 0 : R  = q, where R is J x K with rank(R)=J, the F- statistic defined as is distributed as F(J,n-K), the F distribution with J and n-K degrees of freedom.

11. Discussions Residuals e = y-Xb ~ N(0,  2 M) if  2 is known and M = I-X(X′X) -1 X′ If  2 is unknown and estimated by s 2,

12. Discussions Wald Principle vs. Likelihood Principle: By comparing the restricted (R) and unrestricted (UR) least squares, the F- statistic is shown

13. Discussions Testing R 2 = 0: Equivalently, H 0 : R  = q, where J = K-1, and  1 is the unrestricted constant term. The F-statistic follows F(K-1,n-K).

14. Discussions Testing  k = 0: Equivalently, H 0 : R  = q, where F(1,n-K) = b k [Est Var(b)] -1 kk b k t-ratio: t(n-K) = b k /SE(b k )

15. Discussions t vs. F: –t 2 (n-K) = F(1,n-K) under H 0 : R  =q when J=1 –For J > 1, the F test is preferred to multiple t tests Durbin-Watson Test Statistic for Time Series Model: –The conditional distribution, and hence the critical values, of DW depends on X…

16. Maximum Likelihood Assumption 1, 2, 4, and 5 imply y|X ~ N(X ,  2 I n ) The conditional density or likelihood of y given X is

17. Maximum Likelihood Likelihood Function L( ,  2 ) = f(y|X; ,  2 ) Log Likelihood Function

18. Maximum Likelihood ML estimator of ( ,  2 ) = argmax ( ,  ) log L( ,  ), where we set  =  2

19. Maximum Likelihood Suppose Assumptions 1-5 hold. Then the ML estimator of  is the OLS estimator b and ML estimator of  or  2 is

20. Maximum Likelihood Maximum Likelihood Principle –Let  = ( ,  ) –Score: s(  ) =  log L(  )/  –Information Matrix: I(  ) = E(s(  )s(  )′|X) –Information Matrix Equality:

21. Maximum Likelihood Maximum Likelihood Principle –Cramer-Rao Bound: I(  ) -1 That is, for an unbiased estimator of  with a finite variance-covariance matrix:

22. Maximum Likelihood Under Assumptions 1-5, the ML or OLS estimator b of  with variance  2 (X′X) -1 attains the Cramer- Rao bound. ML estimator of  2 is biased, so the Cramer-Rao bound does not apply. OLS estimator of  2, s 2 = e′e/(n-K) with E(s 2 |X) =  2 and Var(s 2 |X) = 2  4 /(n-K), does not attain the Cramer-Rao bound 2  4 /n.

23. Discussions Concentrated Log Likelihood Function Therefore, argmax  log L(  ) = argmin  SSR(  )

24. Discussions Hypothesis Testing H 0 : R  = q –Likelihood Ratio Test –F Test as a Likelihood Ratio Test

25. Discussions Quasi-Maximum Likelihood –Without normality (Assumption 5), there is no guarantee that ML estimator of  is OLS or that the OLS estimator b achieves the Cramer-Rao bound. –However, b is a quasi- (or pseudo-) maximum likelihood estimator, an estimator that maximizes a misspecified (normal) likelihood function.

26. Generalized Least Squares Assumption 4 Revisited: E(  ′  |X) = Var(  |X) =  2 I n Assumption 4 Relaxed (Assumption 4’): E(  ′  |X) = Var(  |X) =  2 V(X), with nonsingular and known V(X). –OLS estimator of , b=(X′X) -1 X′y, is not efficient although it is still unbiased. –t-test and F-test are no longer valid.

27. Generalized Least Squares Since V=V(X) is known, V -1 = C′C Let y * = Cy, X * = CX,  * = C  y = X  +   y * = X *  +  * –Checking A.2: E(  * |X * ) = E(  * |X) = 0 –Checking A.4: E(  *  * ′|X * ) = E(  *  * ′|X) =  2 CVC′ =  2 I n GLS: OLS for the transformed model y * = X *  +  *

28. Generalized Least Squares b GLS = (X * ′X * ) -1 X * ′y * = (X′V -1 X) -1 X′V -1 y Var(b GLS |X) =  2 (X * ′X * ) -1 =  2 (X′V -1 X) -1 If V = V(X) = Var(  |X)/  2 is known, –b GLS = (X′[Var(  |X)] -1 X) -1 X′[Var(  |X)] -1 y –Var(b GLS |X) = (X′[Var(  |X)] -1 X) -1 –GLS estimator b GLS of  is BLUE.

29. Generalized Least Squares Under Assumption 1-3, E(b GLS |X) = . Under Assumption 1-3, and 4’, Var(b GLS |X) =  2 (X′V(X) -1 X) -1 Under Assumption 1-3, and 4’, the GLS estimator is efficient in that the conditional variance of any unbiased estimator that is linear in y is greater than or equal to [Var(b GLS |X)].

30. Discussions Weighted Least Squares (WLS) –Assumption 4”: V(X) is a diagonal matrix, or E(  i 2 |X) = Var(  i |X) =  2 v i (X) Then –WLS is a special case of GLS.

31. Discussions If V = V(X) is not known, we can estimate its functional form from the sample. This approach is called the Feasible GLS. V becomes a random variable, then very little is known about the distribution and finite sample properties of the GLS estimator.

32. Example Cobb-Douglas Cost Function for Electricity Generation (Christensen and Greene [1976]) Data: Greene’s Table F4.3 –Id = Observation, 123 + 35 holding companies –Year = 1970 for all observations –Cost = Total cost, –Q = Total output, –Pl = Wage rate, –Sl = Cost share for labor, –Pk = Capital price index, –Sk = Cost share for capital, –Pf = Fuel price, –Sf = Cost share for fuel

33. Example Cobb-Douglas Cost Function for Electricity Generation (Christensen and Greene [1976]) –ln(Cost) =  1 +  2 ln(PL) +  3 ln(PK) +  4 ln(PF) +  5 ln(Q) + ½  6 ln(Q)^2 +  7 ln(Q)*ln(PL) +  8 ln(Q)*ln(PK) +  9 ln(Q)*ln(PF) +  –Linear Homogeneity in Prices:  2 +  3 +  4 =1,  7 +  8 +  9 =0 –Imposing Restrictions: ln(Cost/PF) =  1 +  2 ln(PL/PF) +  3 ln(PK/PF) +  5 ln(Q) + ½  6 ln(Q)^2 +  7 ln(Q)*ln(PL/PF) +  8 ln(Q)*ln(PK/PF) + 