cross-classified/cross-nested model: How to get all relevant random components
Posted: Sun Jul 29, 2018 12:46 pm
Dear Madam or Sir,
I am running a cross-classified / cross-nested model in runmlwin and have some issues with the cross-classification for which I am seeking your help:
Geo1 and Geo2 are cross-classified, i.e. it is NOT say counties within states, but any combination of them is possible. At the Geo1 / Geo2 level, there is a time dimension, moreover, there are two non-hierarchically nested geographical identifiers, and a large number of individuals nested in Geo1, Geo2. Individuals are nested within years, but it is a repeated cross-section, not panel of individuals.
The challenge is: While I have specified mcmc(cc) to get the cross-classification, and while I read in this forum that automatically all cross-classifications are made, I only get the random parts specified above, but not f.e. yearXGeo2, yearXGeo1, Geo1XGeo2, which however would also be needed to account for nesting as far as I know?
Should I generate them manually? If they are not part of the output, were they corrected for in the initial estimation?
I have no particular interest in knowing about Geo1Xyear, but since individuals are more similar in the same geo1-year, I would want to have a random effect there, as well as for all other combinations that are relevant given the data structure.
Thank you very much for your time!
Best
I am running a cross-classified / cross-nested model in runmlwin and have some issues with the cross-classification for which I am seeking your help:
Code: Select all
runmlwin DV cons, level4(year: cons) level3(Geo1: cons) level2(Geo2: cons) level1(ID: cons) mcmc(cc) initsb(b) nopause
The challenge is: While I have specified mcmc(cc) to get the cross-classification, and while I read in this forum that automatically all cross-classifications are made, I only get the random parts specified above, but not f.e. yearXGeo2, yearXGeo1, Geo1XGeo2, which however would also be needed to account for nesting as far as I know?
Should I generate them manually? If they are not part of the output, were they corrected for in the initial estimation?
I have no particular interest in knowing about Geo1Xyear, but since individuals are more similar in the same geo1-year, I would want to have a random effect there, as well as for all other combinations that are relevant given the data structure.
Thank you very much for your time!
Best