Course Econometrics I

Transcription

1 Course Econometrics I 4. Heteroskedasticity Martin Halla Johannes Kepler University of Linz Department of Economics Last update: May 6, 2014 Martin Halla CS Econometrics I 4 1/31

2 Our agenda for today Consequences of Heteroskedasticity (H) H-robust inference Testing for H Weighted least squares estimation Form of H know; obs. with higher variance less weight Feasible generalized least squares estimation Form of H is estimated The linear probability model revisited Martin Halla CS Econometrics I 4 2/31

3 Definition of H The homoskedasticity assumption states that the variance of the error term, u, conditional on the explanatory vars, is constant. Var(u x 1, x 2,..., x k ) = σ 2 Put differently, the error has the same variance given any value of the explanatory vars. Whenever the variance of u changes across different values of the explanatory vars., H is present. More generally, we have H, when the error variance differs across units i Var(u i ) = σ 2 i. For example, in a savings eq. H is present, if the variance of u affecting savings increases with income (i. e. there is a higher variance among high income individuals). Martin Halla CS Econometrics I 4 3/31

4 Consequences of H H does not cause bias or inconsistency in OLS. Goodness-of-fit measures are also unaffected. However, usual OLS t-statistics do not have a t distribution. The same applies to F statistics and LM statistics. This is not solved by a larger sample size. We have a problem with statistical inference. Further, OLS is not BLUE; there are more efficient estimators. Intuitively, OLS puts too much weight on obs. with a high error variance. Solutions: Simplest solution: H-robust inference. Given that we know the form of H (or we can estimate it), we can derive a more efficient estimator. Martin Halla CS Econometrics I 4 4/31

5 H-robust inference I Good news: OLS is still useful in the presence of H Econometricians developed in the 1980s methods to adjust s.e. and t, F and LM statistics such that they are valid in the presence of H of unknown form. That means, we can provide valid inference that works in any case. The derivation of this adjusted testing statistics is quite technical, but the application is easy. Martin Halla CS Econometrics I 4 5/31

6 H-robust inference I White (1980, ECM) has shown that under MLR.1 through MLR.4, Var( ˆβ j ) = n i=1 ˆr2 ijû2 i SSR 2 j (1) is a valid estimator for Var( ˆβ j ), where ˆr ij denotes the i th residual from regressing x j on all other indep. vars, and SSR j is the sum of squared residual from this regression. The square root of (1) is the H-robust s.e. for ˆβ j. Sometimes also called White, Huber, Eicker s.e. (or some hyphenated combination of these names); or simply robust s.e. Robust s.e. can be either larger or smaller than usual s.e. Based on (1) we can derive a H-robust t statistic: t = (estimate hyp. value)/s.e. This can easily be done in Stata. Martin Halla CS Econometrics I 4 6/31

7 Example with H-robust inference. reg lwage marrmale marrfem singfem educ exper expersq tenure tenursq, robust Linear regression Number of obs = 526 F( 8, 517) = Prob > F = R-squared = Root MSE = Robust lwage Coef. Std. Err. t P> t [95% Conf. Interval] marrmale marrfem singfem educ exper expersq tenure tenursq _cons In Stata robust standard errors can be easily obtained by the option robust. Martin Halla CS Econometrics I 4 7/31

8 H-robust inference II (I) (II) Coeff. Normal Coeff. Robust s.e. s.e. marrmale 0.213*** (0.055) 0.213*** [0.057] marrfem *** (0.058) *** [0.059] singfem * (0.056) [0.057] educ 0.079*** (0.007) 0.079*** [0.007] exper 0.027*** (0.005) 0.027*** [0.005] exper *** (0.001) *** [0.001] tenure 0.029*** (0.007) 0.029*** [0.007] tenure * (0.001) * [0.001] Constant 0.321** (0.100) 0.321** [0.109] R-squared N In this case usual and robust s.e. are very similar. However, in other cases it might change important conclusions. Martin Halla CS Econometrics I 4 8/31

9 Testing for H Why test? We could simply use only robust s.e.? Under H OLS is not BLUE. Usual t statistics have exact t distrib. under the CLM assumps. Robust s.e./statistics are only valid in large samples. There are many different tests. General idea: test assump. MLR.5: H 0 : Var(u x) = σ 2 If we cannot reject this null hyp., we conclude H is not a problem. Since Var(u x) = E(u 2 x), we can write H 0 : E(u 2 x) = σ 2. That means, we can test whether u 2 is related (in expected value) to any of the x: u 2 = δ 0 + δ 1 x 1 + δ 2 x δ k x k + v The null hyp. of homoskedasticity is H 0 : δ 1 =... = δ k = 0. Implementation with the estimate of u 2, the squared residual û 2. Martin Halla CS Econometrics I 4 9/31

10 The Breusch-Pagan test for H 1. Estimate your model by OLS and obtain û 2 i for each i. 2. Run û 2 = δ 0 + δ 1 x δ k x k + v and save the R-squared (Rû 2 ) Form either the F statistic or the LM statistic F = R 2 û 2 /k (1 R 2 û 2 )/(n k 1) LM = n R 2 û 2 where k is equal to the no. of regressors and n the no. of obs. 4. If the respective p-value is sufficiently small, reject the null-hyp. of homoskedasticity. (If you suspect H only in a sub-set of your indep. vars, you can modify step 2.) Martin Halla CS Econometrics I 4 10/31

11 Breusch-Pagan test for H an example I * Breusch-Pagan test for heteroskedasticity (for Example 8.4) * 1.) Estimate the model by OLS and obtain the squared OLS residuals: qui reg price lotsize sqrft bdrms predict u, resid gen u2=u^2 * 2.) Run the following regression and keep the R-squared: qui reg u2 lotsize sqrft bdrms * 3a.) Form either the F (or the LM) statistic and compute the p-value: * F statistic: display (e(r2)/(1- e(r2))*(84/3)) * P-value display 1-F(3,88,e(F)) » A p-value of suggests strong evidence against homoskedasticity. Martin Halla CS Econometrics I 4 11/31

12 Breusch-Pagan test for H an example II * 3b.) Alternatively we can compute the LM statistic * LM statistic: display e(r2)*e(n) * P-value display 1-chi2(3, ) » Again, strong evidence against the null-hyp. of homoskedasticity. Martin Halla CS Econometrics I 4 12/31

13 Breusch-Pagan test for H an example III Let us consider a model with log transformations of the some variable: drop u u2 qui reg lprice llotsize lsqrft bdrms predict u, resid gen u2=u^2 reg qui u2 llotsize lsqrft bdrms display (e(r2)/(1- e(r2))*(84/3)) display e(f) display 1-F(3,88,e(F)) » Now we fail to reject the null hypothesis of homoskedasticity. Martin Halla CS Econometrics I 4 13/31

14 The White Test (special case) for H 1. Estimate your model by OLS and obtain the residuals and the fitted values and compute also theirs squares (û 2 i, ŷ2 i ). 2. Run û 2 = δ 0 + δ 1 ŷ + δ 2 ŷ 2 + v and save the R-squared (R 2 û 2 ). 3. Form either the F statistic of the LM statistic F 2,n 3 = R 2 û 2 /2 (1 R 2 û 2 )/(n 3) where n is the no. of obs. LM = n R 2 û 2 4. If the respective p-value is sufficiently small, reject the null-hyp. of homoskedasticity. (In the original form of the test you include in step 2. all indep vars x j, their squares x 2 j, and all their cross-products x jx h for j h.) Martin Halla CS Econometrics I 4 14/31

15 The special case of the White Test an example I * Special case of the White Test for heteroskedasticity (see Example 8.5) * 1.) Estimate the model by OLS and obtain the residuals and the fitted values. qui reg lprice llotsize lsqrft bdrms predict u, resid predict fitted, xb /*... and compute also their squares */ gen u2=u^2 gen fitted2=fitted^2 Martin Halla CS Econometrics I 4 15/31

16 The special case of the White Test an example II * 2.) Run the following regression and keep the R-squared: qui reg u2 fitted fitted2 * 3.) Form either the F (or the LM) statistic and compute the p-value: display (e(r2)/(1- e(r2))*(88/2)) display 1-F(2,88,e(F)) * The p-value of provide little evidence against homoskedasticity. * LM statistic display e(r2)*e(n) display 1-chi2(2, ) Martin Halla CS Econometrics I 4 16/31

17 Weighted least squares estimation I Before robust s.e. were available, econometricians used a weighted least squares (WLS) estimation in the presence of H. WLS requires the knowledge of the functional form of the variance. Idea: if we can specify H (as a function of the x), the WLS estimation transforms the estimation model such that we get homoskedastic errors. Under a correct specification of the variance, WLS is more efficient than OLS, and leads to new t and F statistics with correct distributions. WLS is an example for a generalized least squares (GLS) estimation. We can also estimate the form of H before we apply WLS; this procedure is called feasible GLS (FGLS). Martin Halla CS Econometrics I 4 17/31

18 Weighted least squares estimation II Let x denote our RHS vars and assume Var(u x) = σ 2 h(x), (2) where h(x) is some known function that determines H. Of course, σ 2 is unknown, but we will estimate it. For instance, consider the simple savings function sav i = β 0 + β 1 inc i + u i, (3) where assume that the variance of the error is proport. to income Var(u i inc i ) = σ 2 inc i. (4) That means, as income increases the variability in savings increases. We can use this idea to estimate an eq. with heteroskedastic errors, y i = β 0 + β 1 x i β k x ik + u i, (5) and transform it into an eq. that has a homoskedastic error term. Martin Halla CS Econometrics I 4 18/31

19 Weighted least squares estimation III We simply divide the original equation by h i : y i / h i = β 0 / h i + β 1 (x i1 / h i ) β k (x ik / h i ) + (u i / h i ), (6) or y i = β 0 x i0 + β 1 x i β k x ik + u i, (7) where x i0 = 1/ h i and the other starred vars denote the corresponding original vars divided by h i. Note, since Var(u i x i ) = E(u 2 i x i), we can write ( E (u i / h i ) 2) = 1 E(u 2 i ) = 1 (σ 2 h i ) = σ 2, (8) h i h i which means that the error term of the transformed eq. is homoskedastic. Given that the original eq. fulfills MLR.1-4; this eq. fulfills MLR.1-5; savings eq.: sav i / inc i = β 0 (1/ inc i ) + β 1 inci + u i ) Martin Halla CS Econometrics I 4 19/31

20 Weighted least squares estimation IV The OLS estimator gives equal weight to all obs. and minimizes: n = (y i β 0 β 1 x i... β k x k ) 2. (9) i=1 In the transformed model from above we minimize: n ( ) y i β 0 β 1 x i... β k x k hi hi hi hi i=1 n 1 = (y i β 0 β 1 x i... β k x k ) 2 h i=1 i n = w i (y i β 0 β 1 x i... β k x k ) 2. i=1 (10) WLS gives less weight (w i = 1/h i ) to obs. with a higher error var. Martin Halla CS Econometrics I 4 20/31

21 Weighted least squares estimation Example 8.6 * WLS (where we assume that h=inc): * 1. option using transformed vars gen cons_wls = 1/(inc)^(1/2) gen sav_wls = sav/(inc)^(1/2) gen inc_wls = inc/(inc)^(1/2) reg sav_wls inc_wls cons_wls, nocons sav_wls Coef. Std. Err. t P> t [95% Conf. Interval] inc_wls cons_wls * 2. option using Stata s weight option reg sav inc [aw = 1/inc] (sum of wgt is e-02) sav Coef. Std. Err. t P> t [95% Conf. Interval] inc _cons Martin Halla CS Econometrics I 4 21/31

22 Weighted least squares estimation V What are the properties of WLS if our choice for h(x) is incorrect? Just like OLS, WLS still provides an unbiased and consistent estimator. Note, OLS is the special case where we erroneously assumed h(x) = 1. However, the test statistics are no longer valid. Wooldridge argues that even a wrong specification of (strong) H might be better than complete ignorance (by OLS). In case of averaged data (e. g. on a firm-level or country-level) you should always use WLS with 1/h i = m i, where m i is the number of underlying individuals in the ith aggregate unit. Idea: Larger aggregate units have a smaller error variance, and receive a higher weight. Martin Halla CS Econometrics I 4 22/31

23 Feasible GLS Usually, the exact form of H is not obvious. How do we find the function h(x i )? Feasible GLS (FGLS) suggests to use an estimate of h i, denoted as ĥi, in the GLS transformation. FGLS is sometimes also called estimated GLS. Of course, there are many ways to model H. For instance, we could assume that Var(u x) = σ 2 exp(δ 0 + δ 1 x δ k x k ) That means, h(x) = exp(δ0 + δ 1 x δ k x k ) The exponential func. guarantees positive values (for estimated variances). Next slide outlines a corresponding feasible GLS procedure Martin Halla CS Econometrics I 4 23/31

24 A feasible GLS procedure to correct for H 1. Estimate your model and obtain the residual, û 2. Create log(û 2 ) 3. Run the regression of log(û 2 ) on x, and obtain the fitted values, ĝ 4. Exponentiate the fitted values: exp(ĝ) ĥ 5. Estimate your model by WLS, using weights 1/ĥ However, note FGLS is not unbiased. It is only consistent and asymptotically more efficient than OLS. Martin Halla CS Econometrics I 4 24/31

25 Feasible GLS estimation Example 8.7 * 1.) Estimate the model and obtain the residual: qui reg cigs lincome lcigpric educ age agesq restaurn predict u, resid * 2.) Create the log of the squared residual: gen lu2 = log(u^2) * 3.) Run the following regression and obtain the fitted values: qui reg lu2 lincome lcigpric educ age agesq restaurn predict fitted, xb * 4.) Exponentiate the fitted values: gen h = exp(fitted) * 5.) Estimate the model by WLS, using weights $1/h$ reg cigs lincome lcigpric educ age agesq restaurn [aw = 1/h] (sum of wgt is e+01)... cigs Coef. Std. Err. t P> t [95% Conf. Interval] lincome lcigpric educ age agesq restaurn _cons Martin Halla CS Econometrics I 4 25/31

26 The linear probability model revisited Problem: A LPM generally contains H. Solution I: Simply compute robust s.e. Solution II: Estimate the variance and use WLS Var(y x) = p(x))[1 p(x)] Estimate by ĥi = ŷ i (1 ŷ i ). Martin Halla CS Econometrics I 4 26/31

27 The LPM revisited Solution I (Example 8.8) inlf Coef. Std. Err. t P> t [95% Conf. Interval] nwifeinc educ exper expersq age kidslt kidsge _cons Robust inlf Coef. Std. Err. t P> t [95% Conf. Interval] nwifeinc educ exper expersq age kidslt kidsge _cons Martin Halla CS Econometrics I 4 27/31

28 The LPM revisited Solution II Estimating the LPM by weighted least squares 1.) Estimate the model by OLS and obtain the fitted values, ŷ i 2.) Determine whether all ŷ i are inside the interval [0,1] If so, proceed to step 3.). If not, some adjustment is needed to bring all ŷi into the unit interval 3.) Construct the estimated variances in ĥ = ŷ i(1 ŷ i ) 4.) Estimate the following eq. by WLS, using 1/ĥ y = β 0 + β 1 x β k x k + u. Martin Halla CS Econometrics I 4 28/31

29 Estimating the LPM by WLS Example 8.9 (part I) * 1.) Estimate the model by OLS and obtain the fitted values. qui reg PC hsgpa ACT parcoll predict fitted, xb * 2.) Determine whether all fitted values are inside the interval [0,1] sum fitted Variable Obs Mean Std. Dev. Min Max fitted * 3.) Construct the estimated variances. gen h = fitted*(1-fitted) Martin Halla CS Econometrics I 4 29/31

30 Estimating the LPM by WLS Example 8.9 (part II) * 4.) Estimate the following eq. by WLS, using $1/h$ reg PC hsgpa ACT parcoll [w=1/h] (analytic weights assumed) (sum of wgt is e+02) Source SS df MS Number of obs = F( 3, 137) = 2.22 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = PC Coef. Std. Err. t P> t [95% Conf. Interval] hsgpa ACT parcoll _cons Martin Halla CS Econometrics I 4 30/31

31 Estimating the LPM by WLS Example 8.9 (part III) Estimation by OLS: PC Coef. Std. Err. t P> t [95% Conf. Interval] hsgpa ACT parcoll _cons Estimation by WLS: PC Coef. Std. Err. t P> t [95% Conf. Interval] hsgpa ACT parcoll _cons There are no important diffs. The only significant RHS var is parcoll, and in both case the estimated prob. of PC ownership is about 22 percent higher if at least one parent has attended college. Martin Halla CS Econometrics I 4 31/31