In conditional logit model attributes of the alternatives and characteristic of the choosers are included as explanatory variables. However, let us suppose that the choice can be made in different context or choice situation that can affect the choice?can these attributes be included as independent variable? (so adding variables that are not related neither to the agent's characteristics nor to the attributes of the alternative)?
Thanks
If you are able to define all the conditional probability density functions and mass functions, there seems to be no problems with estimation using MCMC methods.
It depends on what you want to estimate. In the standard MNL model, whatever is not measured by observed variables (characteristics and attributes) is absorbed into the random extreme value component. In order to identify any effect you have to go somehow through the observed variables. Say you are interested in the effects of different locations or time periods. Then you might introduce location and/or time variables in interaction with personal characteristics and/or choice attributes. However, the effects of those variables at the end will consists of affecting the coefficients of characteristics and attributes. Alternatively, you might perform different estimations of the same model using datasets collected in different contexts. The effect of different contexts will show up in different parameter estimates.
Using the model and associated methods that I have proposed in my book "Consequential Macroeconomics" (see my list of publications here) it is seen that the need for decision making begins when our social system becomes unbalanced or out of equilibrium. The description of how this should be done takes up 4 chapters and an appendix, and it will provide a general solution to the situation. However a would be seeker of this knowledge needs to read the earlier chapters first!
there is nothing in MNL that "knows" that your variable refers to a given respondent features or to a context variable. In other words, the math works fine with the one or the other (provided you know the trick of how to set different values accross alternatives for a given choice situation, otherwise the estimation is impossible).
But as we are from the same institution we could discuss this directly rather than on social media !
Estimating separate models for different contexts or situations may increase the hassle of interpreting these models and in application of models too.
In my view, you better include such factors as an independent variable to estimate the choice model.
Regards
Ashu