# Calibration issuees

Can anyone suggest a good source on how to calibrate parameters in a model, particularly when parameters are novel and have no guidance in terms of reference to other good papers. For example,
I am currently struggling to understand how Lin in her paper ‘Rating Systems and Procyclicality:an Evaluation in a DSGE Framework’ has calibrated the pecuniary and non-pecuniary default penalties (page 16). Further, Wont there be any identification issues if the model is estimated?

The paper is attached for your reference.

To pinpoint matters, in my model, I have a FOC which reads

(c_p-a_p*c_p(-1)) / ((1-a_p)eps_z_p)=w_pn_p/eps_l_p; which boils down to

c_p /eps_z_p = alphay/(xeps_l_p) (the LHS comes from firms optimisation) in steady state

I calibrated alpha as 0.5 and x (goods mkt mark up) as 1.2. Then I calibrated eps_z_p (preference par as 1) and then worked out the value of eps_l_p as 0.83 as I fixed the share of consumption to income as 0.5. Is this the right way to do it. Or is there a better way? Wont this lead to collinearity issues while estimating the model? The model solves and gets steady state and impulses but I am having issues estimating it.
RatingSystemsAndProcyclicalityAnEv_preview (1).pdf (665 KB)

True “calibration” historically meant setting parameters to match growth facts and then evaluate the model on business cycle facts. Latter uses rather are moment matching techniques: you fix the parameter to obtain a particular steady state value of volatility. There is no general guidance. You only need to make sure that you do not try to evaluate the fit of your model using stuff that you set this way as the reasoning will be circular.

Regarding identification: looking at what you describe it seems that eps_l_p is not an independent parameter you can estimate. Rather, by fixing the consumption to income ratio, you fix this value.