Question

Which assumption is violated when there is heteroscedasticity in dataset? Describe any three methods of detection of heteroscedasticity. 

09 Sep 2024
Answer :
Word Count : 863

In econometrics, one of the core assumptions of the classical linear regression model (CLRM) is that the error terms, or disturbances, exhibit constant variance, also referred to as homoscedasticity. When this assumption is violated, meaning the variance of the error terms varies across observations, it results in a problem known as heteroscedasticity. Heteroscedasticity can lead to inefficient estimates and distortions in hypothesis testing, particularly the standard errors of the estimated coefficients, which affect the t-statistics and p-values.

### Assumption Violated with Heteroscedasticity

In the context of Ordinary Least Squares (OLS) regression, the assumption that is violated when heteroscedasticity is present is the homoscedasticity assumption. Specifically, this assumption states that for all values of the independent variables (X), the variance of the error term (ε) should be constant. Mathematically, this can be expressed as:

\[
\text{Var}(\epsilon_i | X_i) = \sigma^2
\]

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