Chartered Financial Analyst (CFA) Practice Exam Level 2

The Breusch-Pagan Test is specifically designed to detect the presence of conditional heteroskedasticity in the error terms of a regression model. Conditional heteroskedasticity occurs when the variance of the error terms varies across different levels of the independent variables, which can undermine the standard assumptions of ordinary least squares (OLS) regression.

When the error terms exhibit heteroskedasticity, the OLS estimators remain unbiased, but they become inefficient, leading to unreliable hypothesis tests and confidence intervals. The Breusch-Pagan Test assesses whether the squared residuals from the regression can be explained by one or more independent variables in the model. If the test indicates that there is a systematic pattern in the residuals, it suggests the presence of heteroskedasticity, prompting the analyst to take appropriate steps to correct for it.

In contrast, options related to autocorrelation, influential data points, or the normal distribution of residuals involve different aspects of regression analysis and address different issues than what the Breusch-Pagan Test targets. Understanding the specific focus of the Breusch-Pagan Test helps highlight its importance in ensuring the reliability of regression analysis results.