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Part 7: Estimating the Variance of b 7-1/53 Econometrics I Professor William Greene Stern School of Business Department of Economics.

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Presentation on theme: "Part 7: Estimating the Variance of b 7-1/53 Econometrics I Professor William Greene Stern School of Business Department of Economics."— Presentation transcript:

1 Part 7: Estimating the Variance of b 7-1/53 Econometrics I Professor William Greene Stern School of Business Department of Economics

2 Part 7: Estimating the Variance of b 7-2/53 Econometrics I Part 7 – Estimating the Variance of b

3 Part 7: Estimating the Variance of b 7-3/53 Context The true variance of b|X is  2 (XX) -1. We consider how to use the sample data to estimate this matrix. The ultimate objectives are to form interval estimates for regression slopes and to test hypotheses about them. Both require estimates of the variability of the distribution. We then examine a factor which affects how "large" this variance is, multicollinearity.

4 Part 7: Estimating the Variance of b 7-4/53 Estimating  2 Using the residuals instead of the disturbances: The natural estimator: ee/N as a sample surrogate for  /n Imperfect observation of  i, e i =  i - (  - b)x i Downward bias of ee/N. We obtain the result E[ee|X] = (N-K)  2

5 Part 7: Estimating the Variance of b 7-5/53 Expectation of ee

6 Part 7: Estimating the Variance of b 7-6/53 Method 1:

7 Part 7: Estimating the Variance of b 7-7/53 Estimating σ 2 The unbiased estimator is s 2 = ee/(N-K). “Degrees of freedom correction” Therefore, the unbiased estimator of  2 is s 2 = ee/(N-K)

8 Part 7: Estimating the Variance of b 7-8/53 Method 2: Some Matrix Algebra

9 Part 7: Estimating the Variance of b 7-9/53 Decomposing M

10 Part 7: Estimating the Variance of b 7-10/53 Example: Characteristic Roots of a Correlation Matrix

11 Part 7: Estimating the Variance of b 7-11/53

12 Part 7: Estimating the Variance of b 7-12/53 Gasoline Data

13 Part 7: Estimating the Variance of b 7-13/53 X’X and its Roots

14 Part 7: Estimating the Variance of b 7-14/53 Var[b|X] Estimating the Covariance Matrix for b|X The true covariance matrix is  2 (X’X) -1 The natural estimator is s 2 (X’X) -1 “Standard errors” of the individual coefficients are the square roots of the diagonal elements.

15 Part 7: Estimating the Variance of b 7-15/53 X’X (X’X) -1 s 2 (X’X) -1

16 Part 7: Estimating the Variance of b 7-16/53 Standard Regression Results Ordinary least squares regression LHS=G Mean = Standard deviation = Number of observs. = 36 Model size Parameters = 7 Degrees of freedom = 29 Residuals Sum of squares = Standard error of e = <= sqr[ /(36 – 7)] Fit R-squared = Adjusted R-squared = Variable| Coefficient Standard Error t-ratio P[|T|>t] Mean of X Constant| PG| *** Y|.02365*** TREND| ** PNC| PUC| PPT| **

17 Part 7: Estimating the Variance of b 7-17/53 Bootstrapping Some assumptions that underlie it - the sampling mechanism Method: 1. Estimate using full sample: --> b 2. Repeat R times: Draw N observations from the n, with replacement Estimate  with b(r). 3. Estimate variance with V = (1/R)  r [b(r) - b][b(r) - b]’

18 Part 7: Estimating the Variance of b 7-18/53 Bootstrap Application matr;bboot=init(3,21,0.)$ Store results here name;x=one,y,pg$ Define X regr;lhs=g;rhs=x$ Compute b calc;i=0$ Counter Proc Define procedure regr;lhs=g;rhs=x;quietly$ … Regression matr;{i=i+1};bboot(*,i)=b$... Store b(r) Endproc Ends procedure exec;n=20;bootstrap=b$ 20 bootstrap reps matr;list;bboot' $ Display results

19 Part 7: Estimating the Variance of b 7-19/ Variable| Coefficient Standard Error t-ratio P[|T|>t] Mean of X Constant| *** Y|.03692*** PG| *** Completed 20 bootstrap iterations Results of bootstrap estimation of model. Model has been reestimated 20 times. Means shown below are the means of the bootstrap estimates. Coefficients shown below are the original estimates based on the full sample. bootstrap samples have 36 observations Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Mean of X B001| *** B002|.03692*** B003| *** Results of Bootstrap Procedure

20 Part 7: Estimating the Variance of b 7-20/53 Bootstrap Replications Full sample result Bootstrapped sample results

21 Part 7: Estimating the Variance of b 7-21/53 OLS vs. Least Absolute Deviations Least absolute deviations estimator Residuals Sum of squares = Standard error of e = Fit R-squared = Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Mean of X |Covariance matrix based on 50 replications. Constant| *** Y|.03784*** PG| *** Ordinary least squares regression Residuals Sum of squares = Standard error of e = Standard errors are based on Fit R-squared = bootstrap replications Variable| Coefficient Standard Error t-ratio P[|T|>t] Mean of X Constant| *** Y|.03692*** PG| ***

22 Part 7: Estimating the Variance of b 7-22/53 Quantile Regression: Application of Bootstrap Estimation

23 Part 7: Estimating the Variance of b 7-23/53 Quantile Regression  Q(y|x,  ) =  x,  = quantile  Estimated by linear programming  Q(y|x,.50) =  x,.50  median regression  Median regression estimated by LAD (estimates same parameters as mean regression if symmetric conditional distribution)  Why use quantile (median) regression? Semiparametric Robust to some extensions (heteroscedasticity?) Complete characterization of conditional distribution

24 Part 7: Estimating the Variance of b 7-24/53 Estimated Variance for Quantile Regression  Asymptotic Theory  Bootstrap – an ideal application

25 Part 7: Estimating the Variance of b 7-25/53

26 Part 7: Estimating the Variance of b 7-26/53  =.25  =.50  =.75

27 Part 7: Estimating the Variance of b 7-27/53

28 Part 7: Estimating the Variance of b 7-28/53

29 Part 7: Estimating the Variance of b 7-29/53 Multicollinearity Not “short rank,” which is a deficiency in the model. A characteristic of the data set which affects the covariance matrix. Regardless,  is unbiased. Consider one of the unbiased coefficient estimators of  k. E[b k ] =  k Var[b] =  2 (X’X) -1. The variance of b k is the kth diagonal element of  2 (X’X) -1. We can isolate this with the result in your text. Let [X,z] be [Other xs, x k ] = [X 1,x 2 ] (a convenient notation for the results in the text). We need the residual maker, M X. The general result is that the diagonal element we seek is [zM 1 z] -1, which we know is the reciprocal of the sum of squared residuals in the regression of z on X.

30 Part 7: Estimating the Variance of b 7-30/53 I have a sample of observations in a logit model. Two predictors are highly collinear (pairwaise corr.96; p<.001); vif are about 12 for eachof them; average vif is 2.63; condition number is 10.26; determinant of correlation matrix is ; the two lowest eigen vales are and Centering/standardizing variables does not change the story. Note: most obs are zeros for these two variables; I only have approx 600 non-zero obs for these two variables on a total of obs. Both variable coefficients are significant and must be included in the model (as per specification). -- Do I have a problem of multicollinearity?? -- Does the large sample size attenuate this concern, even if I have a correlation of.96? -- What could I look at to ascertain that the consequences of multi-collinearity are not a problem? -- Is there any reference I might cite, to say that given the sample size, it is not a problem? I hope you might help, because I am really in trouble!!!

31 Part 7: Estimating the Variance of b 7-31/53 Variance of Least Squares

32 Part 7: Estimating the Variance of b 7-32/53 Multicollinearity

33 Part 7: Estimating the Variance of b 7-33/53 Gasoline Market Regression Analysis: logG versus logIncome, logPG The regression equation is logG = logIncome logPG Predictor Coef SE Coef T P Constant logIncome logPG S = R-Sq = 93.6% R-Sq(adj) = 93.4% Analysis of Variance Source DF SS MS F P Regression Residual Error Total

34 Part 7: Estimating the Variance of b 7-34/53 Gasoline Market Regression Analysis: logG versus logIncome, logPG,... The regression equation is logG = logIncome logPG logPNC logPUC logPPT Predictor Coef SE Coef T P Constant logIncome logPG logPNC logPUC logPPT S = R-Sq = 96.0% R-Sq(adj) = 95.6% Analysis of Variance Source DF SS MS F P Regression Residual Error Total The standard error on logIncome doubles when the three variables are added to the equation.

35 Part 7: Estimating the Variance of b 7-35/53 Condition Number and Variance Inflation Factors Condition number larger than 30 is ‘large.’ What does this mean?

36 Part 7: Estimating the Variance of b 7-36/53

37 Part 7: Estimating the Variance of b 7-37/53 The Longley Data

38 Part 7: Estimating the Variance of b 7-38/53 NIST Longley Solution

39 Part 7: Estimating the Variance of b 7-39/53 Excel Longley Solution

40 Part 7: Estimating the Variance of b 7-40/53 The NIST Filipelli Problem

41 Part 7: Estimating the Variance of b 7-41/53 Certified Filipelli Results

42 Part 7: Estimating the Variance of b 7-42/53 Minitab Filipelli Results

43 Part 7: Estimating the Variance of b 7-43/53 Stata Filipelli Results

44 Part 7: Estimating the Variance of b 7-44/53 Even after dropping two (random columns), results are only correct to 1 or 2 digits.

45 Part 7: Estimating the Variance of b 7-45/53 Regression of x2 on all other variables

46 Part 7: Estimating the Variance of b 7-46/53 Using QR Decomposition

47 Part 7: Estimating the Variance of b 7-47/53 Multicollinearity There is no “cure” for collinearity. Estimating something else is not helpful (principal components, for example). There are “measures” of multicollinearity, such as the condition number of X and the variance inflation factor. Best approach: Be cognizant of it. Understand its implications for estimation. What is better: Include a variable that causes collinearity, or drop the variable and suffer from a biased estimator? Mean squared error would be the basis for comparison. Some generalities. Assuming X has full rank, regardless of the condition, b is still unbiased Gauss-Markov still holds

48 Part 7: Estimating the Variance of b 7-48/53 Specification and Functional Form: Nonlinearity

49 Part 7: Estimating the Variance of b 7-49/53 Log Income Equation Ordinary least squares regression LHS=LOGY Mean = Estimated Cov[b1,b2] Standard deviation = Number of observs. = Model size Parameters = 7 Degrees of freedom = Residuals Sum of squares = Standard error of e = Fit R-squared = Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Mean of X AGE|.06225*** AGESQ| *** D Constant| *** MARRIED|.32153*** HHKIDS| *** FEMALE| EDUC|.05542*** Average Age = Estimated Partial effect = – 2(.00074) = Estimated Variance e-6 + 4( ) 2 ( e-10) + 4( )( e-8) = e-08. Estimated standard error =

50 Part 7: Estimating the Variance of b 7-50/53 Specification and Functional Form: Interaction Effect

51 Part 7: Estimating the Variance of b 7-51/53 Interaction Effect Ordinary least squares regression LHS=LOGY Mean = Standard deviation = Number of observs. = Model size Parameters = 4 Degrees of freedom = Residuals Sum of squares = Standard error of e = Fit R-squared = Adjusted R-squared = Model test F[ 3, 27318] (prob) = 82.4(.0000) Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Mean of X Constant| *** AGE|.00227*** FEMALE|.21239*** AGE_FEM| *** Do women earn more than men (in this sample?) The coefficient on FEMALE would suggest so. But, the female “difference” is *Age. At average Age, the effect is ( ) =

52 Part 7: Estimating the Variance of b 7-52/53

53 Part 7: Estimating the Variance of b 7-53/53


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