A computational methodology that improves the accuracy of model parameters in a compact model uses methods and algorithms to self-consistently match independently developed base and stress models by re-fitting the stress model to the data set that generates the base model. The re-fitting algorithm removes any discrepancy between the base model and the stress model as the stress model is applied to the data set obtained from a dimension-scaling macro. Stress offsets for dimension-scaling macro devices are calculated to fit the measured values of the model parameters for the same devices. The process of fitting the model parameters to the data set from the dimension-scaling macro calculates constant, linear, and quadratic coefficients for the model parameters, which are employed to increase the accuracy of the model parameters and of the compact model used in circuit simulations and optimization.