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Heteroskedasticity is the violation of the assumption, made in some linear regression models, that all the errors of the regression have the same variance.

Table of Contents

The linear regression setting

Consider the linear regression[eq1]where:

The assumption

Sometimes we assume that all the error terms have the same variance, that is, [eq2]

When this hypothesis holds, we say that the errors are homoskedastic (or homoscedastic).

The violation

On the contrary, when the errors pertaining to different observations do not have the same variance, the errors are said to be heteroskedastic (or heteroscedastic).

In this case, we also say that the regression suffers from (unconditional) heteroskedasticity.

Conditional heteroskedasticity

In most cases, we make an hypothesis stronger than homoskedasticity, called conditional homoskedasticity:[eq3]where X is the design matrix (i.e., the matrix whose $N$ rows are the vectors of regressors $x_{i}$ for $i=1,ldots ,N$).

In other words, we postulate that the variance of the errors is constant conditional on the design matrix.

When this assumption is violated, the regression is said to suffer from conditional heteroskedasticity.

Sphericity of errors

When the errors are assumed to be not only conditionally homoskedastic, but also conditionally uncorrelated, we can write:[eq4]where epsilon is the $N	imes 1$ vector of error terms and I is the $N	imes N$ identity matrix.

In this case, we say that the errors are spherical.

Consequence for efficiency

The ordinary least squares (OLS) estimator of the vector of regression coefficients $eta $ is[eq5]where $y$ is the $N	imes 1$ vector of observations of the dependent variable and X is design matrix defined above.

The Gauss-Markov theorem states that under certain conditions the OLS estimator is the best linear unbiased estimator (BLUE) of the vector of regression coefficients.

Conditional homoskedasticity is one of the assumptions of the Gauss-Markov theorem.

Therefore, OLS is not guaranteed to be BLUE when a regression suffers from conditional heteroskedasticity.

The Gauss-Markov theorem relies on three assumptions: 1) invertible design matrix; 2) strictly exogenous regressors; 3) spherical errors.

How to recover efficiency: the WLS/GLS estimator

The generalized least squares (GLS) estimator of the vector of regression coefficients $eta $ is[eq6]where[eq7]

If all the conditions of the Gauss-Markov theorem except conditional homoskedasticity are met, OLS is not the BLUE estimator, but the GLS estimator is.

For more details and a proof, see the lecture on the generalized least squares estimator.

When V is diagonal, [eq8] is also called Weighted Least Squares (WLS) estimator.

Consequence for bias

Conditional heteroskedasticity does not per se introduce biases in the OLS estimator.

If conditional homoskedasticity is violated, but the other Gauss-Markov assumptions hold, then the OLS estimator remains unbiased.


Since [eq9]we can write the OLS estimator as[eq10]The conditional expectation of [eq11] is [eq12]where we have used the strict exogeneity assumptions of the Gauss-Markov theorem, namely,[eq13]By the Law of Iterated Expectations, we have[eq14]

Consequence for variance

When all the Gauss-Markov hypotheses hold, the conditional covariance matrix of [eq11] is[eq16]


We have proved above that[eq17]Therefore,[eq18]where we have used the assumption of sphericity (no correlation + conditional homoskedasticity), that is,[eq19]

When only the homoskedasticity condition is violated, then[eq20]


See previous proof.

The latter formula can be used in practice to compute the exact covariance of the OLS estimator only if:

  1. [eq21] is known;

  2. the other assumptions of the Gauss-Markov theorem are satisfied.

Since these two conditions are unlikely to be both met in practice, we usually compute the heteroskedasticity-consistent estimator presented in the next section.

Heteroskedasticity-robust standard errors

Under fairly weak assumptions, we can prove (see here) that the asymptotic covariance matrix of [eq11] is[eq23]where[eq24]is the so-called long-run covariance matrix.

In a sufficiently large sample, the covariance matrix of [eq25] is approximately equal to the asymptotic one, divided by the sample size:[eq26]

The matrix [eq27] is consistently estimated by[eq28]

If the terms of the sequence [eq29] are not serially correlated, the long-run covariance matrix becomes[eq30]and it is consistently estimated by[eq31]where:

Putting the various pieces together, we obtain[eq33]

This popular estimator of the covariance matrix has various names:

The square roots of the diagonal entries of the HCE matrix are estimators of the standard deviations of the regression coefficients. They are called heteroskedasticity-robust standard errors.

Condition for the consistency of White's estimator

Note that White's estimator can be used only if the terms of the sequence [eq34] are uncorrelated (a condition we imposed above).

If the terms of [eq29] are correlated, then we need to use another estimator called heteroskedasticity and autocorrelation consistent (HAC) or Newey-West estimator.

How to detect heteroskedasticity

There are numerous statistical tests that can be used to detect heteroskedasticity, for example:

For an introduction to these tests, you can refer, for example, to Greene (2017) and Gurajati (2017).

Models of conditional heteroskedasticity

When the linear regression is performed on time-series data, there are popular models that can be used to analyze and predict how the variance of the error terms changes through time:

More details

More mathematical details and proofs of the facts stated above can be found in the lectures on:


Greene, W.H., 2017. Econometric analysis, 8th edition, Pearson.

Gujarati, D.N., 2017. Basic econometrics, 5th edition, McGraw-Hill.

White, H., 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48, pp.817-838.

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How to cite

Please cite as:

Taboga, Marco (2021). "Heteroskedasticity", Lectures on probability theory and mathematical statistics. Kindle Direct Publishing. Online appendix.

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