www.cmm.bristol.ac.uk/forum

Hello,

I am new to MCMC techniques so please bear with me if this question is very basic.

I am using the European Social Survey so I am working with individuals nested within countries (15 countries) and several years have been pooled together. (Pooled Time Series Cross Sectional).

I am struggling with strange variance estimates when I run MCMC estimates of my model using MLwiN. When I use RIGLS, the variance components are country level (0.4) and individual level (5.4) but when I use MCMC the country variation goes way up to to 5.2 and the individual variance stays the same (5.4). Plus, it is strange that when I added country-level predictors to the model when using MCMC the country variance increases rather than decreases.
This does not happen when I use RIGLS and there is a strong theoretical basis for including these predictors and they are significant under the maximum liklihood framwork..

Any help would be much appreciated.

Anne-Marie

There is no particular reason why this should happen. Have you checked the convergence diagnostics to ensure that all the chains are stable, as it may just take a while for the model to get to the correct distributions, meaning that you would need to change the burnin and chain length from the defaults?

Thanks for getting back to me with some thoughts.

I have increased the burn-in/iterations (even up to a million iterations) and I still have the same problem.
The coefficients converge (the intercept with greater difficulty but eventually it converges).

The strange thing is that the country-level variance is 5 times bigger than in RIGLS when I run the null model in MCMC.

Any help on this would extremely appreciated!

Dear Anne-Marie,
If you were able to send me the worksheet I'd happily take a look and check that there wasn't anything silly going on.
Best wishes,
Bill.