What does serial correlation measure in a dataset?

Serial correlation, also known as autocorrelation, measures the correlation of error terms within a dataset, specifically how the residuals (or errors) from a statistical model are related to each other. In time series analysis, if the residuals from one period are correlated with residuals from another period, it suggests that the model may not be adequately capturing all the information in the data. This can indicate a potential problem with the model, such as omitted variables or inappropriate functional forms.

Understanding serial correlation is crucial for improving the accuracy of forecasts and ensuring that statistical tests applied to the data yield valid results. By identifying and addressing serial correlation, analysts can refine their models to better predict future values based on past performance, thereby leading to more informed financial decisions.

The other choices focus on different aspects of data analysis or forecasting without addressing the concept of serial correlation directly, making them less relevant to the question.