The Residual Sum of Squares (RSS) is a widely used statistical measure for analyzing the accuracy of a model. RSS measures the level of variance in the residuals, or error terms, of a regression model. It represents the total sum of squared errors from the squared differences from real values to predicted values. Ideally, the residual sum of squares should be as small as possible, indicating a strong correlation between the predicted and real values.

The calculation of RSS consists of first calculating the residuals of each data point and then summing the squared residuals. This can be expressed mathematically using the following equation:

RSS = ∑ (yi – ŷi)2

Where yi is the observed value and ŷi is the predicted value of the i-th data point.

RSS is a valuable tool which financial analysts use to estimate the validity of their econometric models. A smaller residual sum of squares suggests that the econometric models are accurately predicting future movement of an investment and a larger RSS suggests that it is not. A perfect fit would have an RSS value of zero, meaning that the observed and predicted values match perfectly.

As such, RSS is an important indicator for model accuracy and is used in fields such as finance, engineering, and economics. The RSS can be also be used to compare between different models and to determine which model is the more suitable for a particular purpose.

Ultimately, the residual sum of squares is a powerful measure for analyzing the accuracy of regression models. By fitting the model and computing the RSS, financial analysts are able to evaluate the validity of their econometric models and can make better-informed decisions regarding their investments.