Is there a way to
add_criterion in brms() using a memory efficient option, something comparable to the
loo.function method. You see I have a large number of data points, around 300,000, and quite a few parameters (around 700). The model contains firms in countries across time, with random effects (at the country:year level) as well as fixed effects.
Alternatively, do you think it is `fine’ for model comparison purposes to either:
- Use a sub-sample of the fitted model and calculate the WAIC on that; Or
- Divide the fitted model into 3 sub-periods, say 1994-2001; 2002-2007; 2008-2017. And then look at the WAIC for each time period and compare the WAIC for these time periods across different models.
I know K-Fold is being recommended for hierarchical model comparisons but given the size of this model I am not sure if computationally feasible(?)