Chartered Financial Analyst (CFA) Practice Exam Level 2

What does the Durbin Watson Test help detect in time series data?

1. Multi-collinearity

2. Autocorrelation

3. Heteroskedasticity

4. Overfitting of the model

The correct answer is: Autocorrelation

The Durbin-Watson Test is specifically designed to detect the presence of autocorrelation in the residuals of a regression analysis. Autocorrelation occurs when the residuals (errors) from a regression model are correlated across time. This is a common issue in time series data, where the outcome at one point in time can be influenced by previous outcomes. A value of the Durbin-Watson statistic near 2 suggests that there is little to no autocorrelation present. Values significantly lower than 2 may indicate positive autocorrelation, while values significantly higher than 2 may indicate negative autocorrelation. This is crucial for ensuring the validity of the regression results because the presence of autocorrelation can lead to underestimated standard errors and incorrect inference. Other concepts such as multi-collinearity, heteroskedasticity, or overfitting pertain to different issues within regression analysis. Multi-collinearity refers to the correlation between independent variables, which can make it difficult to determine the effect of each variable. Heteroskedasticity refers to the condition where the variability of the residuals is not constant across all levels of an independent variable. Overfitting is related to model complexity, where a model fits the noise rather than the underlying data. Each