Yield curve fitting criteria

Despite its trial and error nature, such a method is easily implemented on a spreadsheet. We make two columns, one containing the experimental data, the other the theoretical curve as calculated with assumed parameter values. In a third column we calculate the squares of the residuals (i.e., the differences between the two), and we add all these squares to form the sum of squares, SRR. This sum of the residuals squared, SRR, will be our data-fitting criterion. We now adjust the various assumed parameters that define the theoretical curve, in such a direction that SRR decreases. We keep doing this for the various parameters until SRR has reached a minimum. Presumably, this minimum yields the best-fitting parameter values. Incidentally, the third column is not needed when we use the command =SUMXMY2[experimental data, theoretical data). 

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