# Heteroscedasticity

## Consequences of Heteroscedasticity

When heteroscedasticity is present in data, then estimates based on Ordinary Least Square (OLS) are subjected to

#### The consequences of Heteroscedasticity are as follows

1. We cannot apply the formula of the variance of the coefficients to conduct tests of significance and construct confidence intervals.
2. If the error term ($\mu_i$) is heteroscedastic, then the OLS estimates do not have the minimum variance property in the class of unbiased estimators, i.e. they are inefficient in small samples. Furthermore, they are asymptotically inefficient.
3. The estimated coefficients remain unbiased statistically. That means the property of unbiasedness of OLS estimation is not violated by the presence of heteroscedasticity.
4. The forecasts based on the model with heteroscedasticity will be less efficient as OLS estimation yields higher values of the variance of the estimated coefficients.

All this means the standard errors will be underestimated and the t-statistics and F-statistics will be inaccurate, caused by several factors, but the main cause is when the variables have substantially different values for each observation. For instance, GDP will suffer from heteroscedasticity if we include large countries such as the USA and small countries such as Cuba. In this case, it may be better to use GDP per person. Also, note that heteroscedasticity tends to affect cross-sectional data more than time series.

Consider the simple linear regression model (SLRM)

The OLS estimate of $\hat{\beta}$ and $\alpha$ are

\begin{align*}
\hat{\beta}&=\frac{\sum x_i y_i}{\sum x_i^2}=\frac{\sum x_i (\beta x_i +\epsilon_i)}{\sum x_i^2}\\
&=\beta\frac{\sum x_i^2}{\sum x_i^2}+\frac{\sum x_i \epsilon_i}{\sum x_i^2}\\
&=\beta + \frac{\sum x_i \epsilon_i}{\sum x_i^2}
\end{align*}

Applying expectations on both sides we get:

$E(\hat{\beta}=\beta+\frac{\sum E(x_i \epsilon_i)}{\sum x_i^2}=\beta \qquad E(\epsilon_i x_i)=0$

Similarly

\begin{align*}\hat{\alpha}&=\overline{y}-\hat{\beta}\overline{X}\\
&=\alpha+\beta\overline{X}+\overline{\epsilon}-\hat{\beta}\overline{X}\\
&=\alpha+\beta\overline{X}+0-\overline{X}\beta=\alpha
\end{align*}

Hence, the unbiasedness property of OLS estimation is not affected by Heteroscedasticity.

Consequences of Heteroscedasticity References

## Remedial Measures of Heteroscedasticity

The post is about Remedial Measures of Heteroscedasticity.

Heteroscedasticity is a condition in which the variance of the residual term, or error term, in a regression model, varies widely.

The heteroscedasticity does not destroy the unbiasedness and consistency properties of the OLS estimator (as OLS estimators remain unbiased and consistent in the presence of heteroscedasticity), but they are no longer efficient, not even asymptotically. The lack of efficiency makes the usual hypothesis testing procedure dubious (مشکوک، غیر معتبر). Therefore, there should be some remedial measures for heteroscedasticity.

### Remedial Measures of Heteroscedasticity

For remedial measures of heteroscedasticity, there are two approaches: (i) when $\sigma_i^2$ is known, and (ii) when $\sigma_i^2$ is unknown.

#### (i) $\sigma_i^2$ is known

Consider the simple linear regression model $Y_i=\alpha + \beta X_i + u_i$.

If $V(u_i)=\sigma_i^2$ 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 Squares (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 the appropriate solution is to transform the original model in such a way that the transformed disturbance term has a constant variance. The transformed model reduces the adjustment of the original data. The transformed error term $u_i$ has a constant variance i.e. homoscedastic. Mathematically

\begin{eqnarray*}
V(u_i^*)&=&V\left(\frac{u_i}{\sigma_i}\right)\\
&=&\frac{1}{\sigma_i^2}Var(u_i)\\
&=&\frac{1}{\sigma_i^2}\sigma_i^2=1
\end{eqnarray*}

This approach has limited use as the individual error variances 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 $\sigma_i^2$.

Let us discuss the second remedy of heteroscedasticity from remedial measures of heteroscedasticity.

#### (ii) $\sigma_i^2$ 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 the hypothesized relationship is $Var(u)=\sigma^2 X_i^2$ (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 $Y_i =\alpha + \beta X_i +u_i$.
\begin{eqnarray*}
\frac{Y_i}{X_i}&=&\frac{\alpha}{X_i}+\beta+\frac{u_i}{X_i}\\
\mbox{where } Y_i^*&=&\frac{Y_i}{X_i}, \alpha_I^*=\frac{1}{X_i} \mbox{and  } u_i^*=\frac{u}{X_i}
\end{eqnarray*}

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

\begin{eqnarray*}
V(u_i^*)&=&V(\frac{u_i}{X_i})\\
&=&\frac{1}{X_i^2} V(u_i^2)\\
&=&\frac{1}{X_i^2}\sigma^2X_i^2\\
&=&\sigma^2=\mbox{ Constant}
\end{eqnarray*}

For remedial measures of heteroscedasticity, some other hypothesized relations are:

• Error variance is proportional to $X_i$ (Square root transformation) i.e $E(u_i^2)=\sigma^2X_i$
The transformed model is
$\frac{Y_i}{\sqrt{X_i}}=\frac{\alpha}{\sqrt{X_i}}+\beta\sqrt{X_i}+\frac{u_i}{\sqrt{X_i}}$
It (the transformed model) has no intercept term. Therefore we have to use the regression through the origin model to estimate $\alpha$ and $\beta$. To get the original model, multiply $\sqrt{X_i}$ with the transformed model.
• Error Variance is proportional to the square of the mean value of $Y$. i.e. $E(u_i^2)=\sigma^2[E(Y_i)]^2$

### Tests for Homoscedasticity

Some tests commonly used for testing the assumption of homoscedasticity are:

Reference:
A. Koutsoyiannis (1972). “Theory of Econometrics”. 2nd Ed.

Conducting Statistical Models in R Language

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