Which analysis provides insights into how close forecasts are to actual results?

Residual analysis is a statistical technique used to assess the accuracy of forecasts by examining the differences between predicted values and the actual results. In the context of budgeting and financial forecasting, it allows financial professionals to evaluate how well their models perform by calculating residuals, which are the errors or deviations of actual outcomes from the forecasts.

By analyzing these residuals, practitioners can identify patterns, biases, or trends that may indicate systematic errors in the forecasting process. This can lead to improvements in forecasting methods, helping to refine the techniques used to produce more accurate financial projections over time.

While other methods, like cost escalation and disaggregation, focus on different aspects of budgeting and forecasting—such as understanding cost behavior or breaking down data into component parts—residual analysis specifically concentrates on the comparison of forecast accuracy and actual economic outcomes. Aggregation involves summing up individual components, which does not provide direct insights into forecast accuracy. Thus, residual analysis stands out as the appropriate method for evaluating forecast performance.