Category Archives: Remedial Measures of Heteroscedasticity

Heteroscedasticity Regression Residual Plot


One of the assumption of classical linear regression model is that there is no heteroscedasticity (error terms has constant error term) meaning that ordinary least square (OLS) estimators are (BLUE, best linear unbiased estimator) and their variances is the lowest of all other unbiased estimators (Gauss Markov Theorem). If the assumption of constant variance does not hold then this means that the Gauss Markov Theorem does not apply. For heteroscedastic data, regression analysis provide unbiased estimate for the relationship between the predictors and the outcome variables.

As we have discussed that heteroscedasticity occurs when the error variance has non-constant variance.  In this case, we can think of the disturbance for each observation as being drawn from a different distribution with a different variance.  Stated equivalently, the variance of the observed value of the dependent variable around the regression line is non-constant.  We can think of each observed value of the dependent variable as being drawn from a different conditional probability distribution with a different conditional variance. A general linear regression model with the assumption of heteroscedasticity can be expressed as follows

y_i & = \beta_0 + \beta_1 X_{i1} + \beta_2 X_{i2} + \cdots + \beta_p X_ip + \varepsilon_i\\
&=\sigma_i^2; \cdots i=1,2,\cdots, n

Note that we have a $i$ subscript attached to sigma squared.  This indicates that the disturbance for each of the $n$-units is drawn from a probability distribution that has a different variance.

If the error term has non-constant variance, but all other assumptions of the classical linear regression model are satisfied, then the consequences of using the OLS estimator to obtain estimates of the population parameters are:

  • The OLS estimator is still unbiased
  • The OLS estimator is inefficient; that is, it is not BLUE
  • The estimated variances and covariances of the OLS estimates are biased and inconsistent
  • Hypothesis tests are not valid

Detection of Heteroscedasticity Regression Residual Plot

The residual for the $i$th observation, $\hat{\varepsilon_i}$, is an unbiased estimate of the unknown and unobservable error for that observation, $\hat{\varepsilon_i}$. Thus the squared residuals, $\hat{\varepsilon_i^2}$ , can be used as an estimate of the unknown and unobservable error variance,  $\sigma_i^2=E(\hat{\varepsilon_i})$.  You can calculate the squared residuals and then plot them against an explanatory variable that you believe might be related to the error variance.  If you believe that the error variance may be related to more than one of the explanatory variables, you can plot the squared residuals against each one of these variables.  Alternatively, you could plot the squared residuals against the fitted value of the dependent variable obtained from the OLS estimates.  Most statistical programs (softwares) have a command to do these residual plots.  It must be emphasized that this is not a formal test for heteroscedasticity.  It would only suggest whether heteroscedasticity may exist.

Below there are residual plots showing the three typical patterns. The first plot shows a random pattern that indicates a good fit for a linear model. The other two plot patterns of residual plots are non-random (U-shaped and inverted U), suggesting a better fit for a non-linear model, than linear regression model.

Heteroscedasticity Regression Residual Plot 3

Heteroscedasticity Regression Residual Plot 1

Heteroscedasticity Residual Plot 1

Heteroscedasticity Residual Residual Plot 2

Heteroscedasticity Residual Plot 2

Heteroscedasticity Regression Residual Plot 3


Download pdf file Heteroscedasticity Regression Residual Plot


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Unbiased and inconsistent estimates in presence of Heteroscedasticity need some remedial measures

Remedial Measures for Heteroscedasticity

The OLS estimators remains unbiased and consistent in the presence of Heteroscedasticity, but they are no longer efficient not even asymptotically. This lack of efficiency makes the usual hypothesis testing procedure of dubious value. Therefore remedial measures may be called. There are two approaches for remedial measures of heteroscedasticity

(i) $\sigma_i^2$ is known

Consider the simple linear regression model Yi=α+βXii.

If $V(\mu_i)=\sigma_i^2$ then heteroscedasticity is present. Given the values of $\sigma_i^2$ heteroscedasticity can be corrected by using weighted least squares (WLS) as a special case of Generalized Least Square (GLS). Weighted least squares is the OLS method of estimation applied to the transformed model.

When heteroscedasticity is detected by any appropriate statistical test, then appropriate solution is transform the original model in such a way that the transformed disturbance term has constant variance. The transformed model reduces to the adjustment of the original data. The transformed error term μi has a constant variance i.e. homoscedastic. Mathematically


This approach has its limited use as the individual error variance are not always known a priori. In case of significant sample information, reasonable guesses of the true error variances can be made and be used for $\sigma_i^2$.

(ii) When $\sigma_i^2$ is unknown

If $\sigma_i^2$ is not known a priori, then heteroscedasticity is corrected by hypothesizing a relationship between the error variance and one of the explanatory variables. There can be several versions of the hypothesized relationship. Suppose hypothesized relationship is $Var(\mu)=\sigma^2 X_i^2$ (error variance is proportional to $X_i^2$). For this hypothesized relation we will use the following transformation to correct for heteroscedasticity for the following simple linear regression model Yi=α+βXii.
\Rightarrow \quad Y_i^*&=&\beta +\alpha_i^*+\mu_i^*\\
\mbox{where } Y_i^*&=&\frac{Y_i}{X_i}, \alpha_I^*=\frac{1}{X_i} \mbox{and  } \mu_i^*=\frac{\mu}{X_i}

Now the OLS estimation of the above transformed model will yield the efficient parameter estimates as $\mu_i^*$’s have constant variance. i.e.

&=&\frac{1}{X_i^2} V(\mu_i^2)\\
&=&\sigma^2=\mbox{ Constant}

For correction of heteroscedasticity some other hypothesized relations are

  • Error variance is proportional to Xi (Square root transformation) i.e $E(\mu_i^2)=\sigma^2X_i$
    The transformed model is
    It (transformed model) has no intercept term. Therefore we have to use the regression through the origin model to estimate $\alpha$ and β.To get original model, multiply $\sqrt{X_i}$ with transformed model.
  • Error Variance is proportional to the square of the mean value of Y. i.e. $E(\mu_i^2)=\sigma^2[E(Y_i)]^2$
    Here the variance of $\mu_i$ is proportional to the square of the expected value of Y, and E(Yi)=α+βxi.
    The transformed model will be
    This transformation is not appropriate because E(Yi) depends upon $\alpha$ and β which are unknown parameters. $\hat{Y_i}=\hat{\alpha}+\hat{\beta}$ is an estimator of E(Yi), so we will proceed in two steps

    1. We run the usual OLS regression dis-regarding the heteroscedasticity problem and obtain $\hat{Y_i}$
    2. We will transform the model by using estimated $\hat{Y_i}$ i.e. $\frac{Y_i}{\hat{Y_i}}=\alpha\frac{1}{\hat{Y_i}}+\beta_1\frac{X_i}{\hat{Y_i}}+\frac{\mu_i}{\hat{Y_i}}$ and run the regression on transformed model.

    This transformation will perform satisfactory results only if the sample size is reasonably large.

  • Log transformation such as ln Yi=α+β ln Xii
    Log transformation compresses the scales in which the variables are measured. But this transformation is not applicable if some of the Y and X values are zero or negative.

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