Why is there a difference in the value of alphas and betas in the model after I calibrate?
Calibration finds the optimal values for a model's parameters.
As a result, the found values are dependent on the initial values, the tolerance setting, and max number of iterations.
The key question is whether the new values give the desired model and if the model's fit (LLF/AIC, etc.) is better or worse.