Hi Robert,

The steady-state ratios are functions of parameters and other variables at their steady-state values, which are constant.

The former are primitive, while the latter are themselves functions of parameters.

Therefore, if you were to lay-out a Matlab script, in the first place you could declare your calibration, then derive in order the steady-state variables, and finally define ratios among them; you would then be able to read these ratios off your workspace.

Say you are interested in the investment-capital ratio, the following would be sufficient, and you would find cik in your workspace (you can of course create a loop if you want to look at several values )

```
ctou=.025;
constepinf=0.7;
constebeta=0.7420;
ctrend=0.3982;
cpie=1+constepinf/100; %gross inflation rate
cgamma=1+ctrend/100 ; %gross growth rate
cbeta=1/(1+constebeta/100); %discount factor
cik=(1-(1-ctou)/cgamma)*cgamma; %i_k: investment-capital ratio
```

This steady-state analysis is of course external to the model, and you could find it interesting *per se*.

Else, you could simply declare steady-state ratios, and the steady-state variables they are made up of, as as additional parameters, and express them as functions as paramters.

Your model file would then look as:

```
parameters
ctou
constepinf
constebeta
ctrend
cpie
cgamma
cbeta
cik
;
ctou=.025;
constepinf=0.7;
constebeta=0.7420;
ctrend=0.3982;
cpie=1+constepinf/100; %gross inflation rate
cgamma=1+ctrend/100 ; %gross growth rate
cbeta=1/(1+constebeta/100); %discount factor
cik=(1-(1-ctou)/cgamma)*cgamma; %i_k: investment-capital ratio
```

With this approach, you would find the value of your ratios of interest, updated as of your calibration, in the `M_.params`

field, in order of declaration in the parameter block. This is innocuous, for example, if you are not going to estimate the model, or more in general if parameter dependencies do not matter.

In the file that you mention, parameters that are functions of other parameters or steady-state variables are conveniently defined as model-local variables, that is variables that only live in the processing of the model file but are not saved in the workspace. This is the reason why you can’ t find them there.

They are useful for at least two reasons; first, when parameters are estimated, parameter dependency is handled correctly, and second they reduce the number of parameters that you actually have to declare.

I personally like to have a separate script for this kind of steady-state analyses, as suggested above, and use model-local variables in the model file, but you may prefer to first not estimate the model and follow method 2, conduct your steady-state analysis, and then handle parameter dependency correctly by switching to the approach with model-local variables when you decide to estimate the model.

As you prefer