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Heteroskedasticity LINEAR REGRESSION ANALYSIS MODULE – X Lecture - 32 Heteroskedasticity Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur 2 In the multiple regression model yX=βε + , it is assumed that 2 VI()εσ= , 22 i. e ., Var (εi )= σ , Cov ( εεij )= 0, i ≠= j 1,2,..., n . In this case, the diagonal elements of covariance matrix of ε are same indicating that the variance of each ε i is same and off-diagonal elements of covariance matrix of ε are zero indicating that all disturbances are pairwise uncorrelated. This property of constancy of variance is termed as homoskedasticity and disturbances are called as homoskedastic disturbances. In many situations, this assumption may not be plausible and the variances may not remain same. The disturbances whose variances are not constant across the observations are called heteroskedastic disturbance and this property is termed as heteroskedasticity. In this case 2 Var(εσii )= , i = 1,2,..., n and disturbances are pairwise uncorrelated. The covariance matrix of disturbances is 2 σ1 00 2 22 2 00σ 2 V(ε )= diag ( σσ12 , ,..., σn ) = . 2 00 σ n Graphically, the following pictures depict homoskedasticity and heteroskedasticity. 3 Homoskedasticity Heteroskedasticity (Var(y) increases with x) Heteroskedasticity (Var(y) decreases with x) 4 Examples Suppose in a simple linear regression model, x denote the income and y denotes the expenditure on food. It is observed that as the income increases, the variation in expenditure on food increases because the choice and varieties in food increases, in general upto certain extent. So the variance of observations on y will not remain constant as income changes. The assumption of homoscedasticity implies that the consumption pattern of food will remain same irrespective of the income of the person. This may not generally be a correct assumption in real situations. Rather the consumption pattern changes and hence the variance of y and so the variances of disturbances will not remain constant. In general, it will be increasing as income increases. In another example, suppose in a simple linear regression model, x denotes the number of hours of practice for typing and y denotes the number of typing errors per page. It is expected that the number of typing mistakes per page decreases as the person practices more. The homoskedastic disturbances assumption implies that the number of errors per page will remain same irrespective of the number of hours of typing practice which may not be true is practice. 5 Possible reasons for heteroskedasticity There are various reasons due to which the heteroskedasticity is introduced in the data. Some of them are as follows: 1. The nature of phenomenon under study may have an increasing or decreasing trend. For example, the variation in consumption pattern on food increases as income increases, similarly the variation in number of typing mistakes decreases as the number of hours of typing practice increases. 2. Sometimes the observations are in the form of averages and this introduces the heteroskedasticity in the model. For example, it is easier to collect data on the expenditure on clothes for the whole family rather than on a particular family member. Suppose in a simple linear regression model yij =++=ββ01 xij ε ij , i 1,2,..., nj , = 1,2,..., mi , th th th yij denotes the expenditure on cloth for the j family having mj members and xij denotes the age of the i person in j family. It is difficult to record data for individual family member but it is easier to get data for the whole family. So yij’s are known collectively. Then instead of per member expenditure, we find the data on average expenditure for each family member as m 1 j yyi = ∑ ij m j j=1 and the model becomes yxi=++ββ01 ii ε. 2 2 σ It we assume E ()0,() ε ij = Var εσ ij = , then E () εε ii = 0, Var () = which indicates that the resultant variance of m j disturbances does not remain constant but depends on the number of members in a family mj. So heteroskedasticity enters in the data. The variance will remain constant only when all mj ’s are same. 6 3. Sometimes the theoretical considerations introduces the heteroskedasticity in the data. For example, suppose in the simple linear model =++=ββ ε yi01 xi ii, 1,2,..., n yi denotes the yield of rice and xi denotes the quantity of fertilizer in an agricultural experiment. It is observed that when the quantity of fertilizer increases, then yield increases. In fact, initially the yield increases when quantity of fertilizer increases. Gradually, the rate of increase slows down and if fertilizer is increased further, the crop burns. So notice that β 1 changes with different levels of fertilizer. In such cases, when β 1 changes, a possible way is to express 2 it as a random variable with constant mean β 1 and constant variance θ like ββ11ii=+=vi, 1,2,..., n with 2 E() vi = 0, Var () vi =θε ,( Eii v ) = 0. So the complete model becomes yxi=++ββ01ii ε i ββ11ii= + v ⇒=+yββ x +() ε + xv i 01i i ii =++ββxw 01ii where wi=ε i + xv ii is like a new random error component. 7 So Ew()0i = 2 Var() wii= E ( w ) 2 22 =++E()εεi x i E ()2 v i xE i ( ii v ) =++σθ2x 22 0 i 2 22 =σθ + xi . So variance depends on i and thus heteroskedasticity is introduced in the model. Note that we assume homoskedastic disturbances for the model yxi=++β01 β iii εβ, 1 =+ β 1 v i but finally ends up with heteroskedastic disturbances. This is due to theoretical considerations. 4. The skewness in the distribution of one or more explanatory variables in the model also causes heteroskedasticity in the model. 5. The incorrect data transformations and incorrect functional form of the model can also give rise to the heteroskedasticity problem. 6. Sometimes the study variable may have distribution such as Binomial or Poisson where variances are the functions of means. In such cases, when the mean varies, then the variance also varies. 8 Tests for heteroskedasticity The presence of heteroskedasticity affects the estimation and test of hypothesis. The heteroskedasticity can enter into the data due to various reasons. The tests for heteroskedasticity assume a specific nature of heteroskedasticity. Various tests are available in literature for testing the presence of heteroskedasticity, e.g., 1. Bartlett test 2. Breusch Pagan test 3. Goldfeld Quandt test 4. Glesjer test 5. Test based on Spearman’s rank correlation coefficient 6. White test 7. Ramsey test 8. Harvey Phillips test 9. Szroeter test 10. Peak test (nonparametric) test. We discuss the Bartlett’s test. 9 Bartlett’s test It is a test for testing the null hypothesis 2222 H01:σσσσ = 2 = ... =in = ... = . This hypothesis is termed as the hypothesis of homoskedasticity. This test can be used only when replicated data is available. Since in the model 2 yi=β11 X i + β 2 Xi 2 ++... βk X ik + ε i , E ( ε i ) = 0, Var ( εσi ) = i , i = 1,2,..., n , 2 only one observation yi is available to find σ i , so the usual tests can not be applied. This problem can be overcome if replicated data is available. So consider the model of the form ** yXii=βε + i * * where y i is a mi x 1 vector, Xi is mi x k matrix, β is k x 1 vector and ε i is mi x 1 vector. So replicated data is now * available for every y i in the following way: ** yX11=βε + 1consists ofm1 observations ** yX22=βε + 2consists ofm2 observations yX**=βε + m nn nconsists ofn observations. 10 All the individual model can be written as ** y11 X1 ε y**X ε 22=2 β + ** ynnX n ε or yX**=βε + nn n where y * is a vector of order ∑∑ m ii ×× 1, X is mk matrix, β is k × 1 vector and ε * is ∑ m i × 1 vector. ii=11 = i=1 Application of OLS to this model yields − βˆ = (XX ' )1 Xy '* and obtain the residual vector ** ˆ eyX.ii = − iβ. Based on this, obtain 1 2 ** si= eeii' mki − n 2 ∑()mii− ks s2 = i=1 . n ∑()mki − i=1 Now apply the Bartlett’s test as 11 1 n s2 χ 2 = − ∑(mki ) log 2 Csi=1 i 2 which has asymptotic χ − distribution with (n – 1) degrees of freedom where 1n 11 =+− C 1.∑n 3(n−− 1) = mk i 1 i ()mk− ∑ i i=1 Another variant of Bartlett’s test Another variant of Bartlett’s test is based on likelihood ratio test statistic. If there are m independent normal random th samples where there are ni observations in the i sample. Then the likelihood ratio test statistic for testing 2222 H 01 : σσσσ = 2 = ... = im = ... = is n /2 m 2 i si u = ∑ 2 i=1 s 2 th where ys ii and are the sample mean and sample variance respectively of the i sample. ni 2 1 2 si =∑( yij −= y i ) , i 1,2,..., mj ; = 1,2,..., ni ni j=1 m 221 s= ∑ nsii n i=1 m nn= ∑ i . i=1 12 2 To obtain an unbiased test and a modification of − 2ln u which is a closer approximation to χm−1 under H 0 , Bartlett test replaces ni by (ni – 1) and divide by a scalar constant. This leads to the statistic m 22 (nm− ) logσσˆˆ −−∑ ( nii 1) log M = i=1 1m 11 1+− ∑ 3(m− 1)i=1 ni −− 1 nm χ 2 which has a distribution with (m - 1) degrees of freedom under H0 and ni 221 σˆi = ∑()yyij− i n −1 j=1 1 m σσˆˆ22= − ∑(nii 1) .
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