The case: I am investigating the impact of various predictors on the odds of migration using a discrete-time event history model within a multilevel framework. The outcome variable is dichotomous (0=not migrated; 1=migrated). The predictors are largely time varying covariates operating at the individual level (e.g., years of schooling, marital status) and community level (e.g., community wealth index, social networks). Individuals are observed during five years. As such, the multilevel-model structure is as follows: Level 1: years of observation; Level 2: individuals; Level 3: communities. I am estimating these models using multilevel logit models (lme4 package) on stacked data (person-year data file).
The problem: Does anyone know whether there is a limit to the number of time varying covariates that I can safely include in these models. Lets assume that I have 1000 individuals (level 2) that are observed up to 5 years leading to 4500 person year records at level 1 (e.g., about 4.5 records per individual). Those 1000 individuals are located in 25 communities (level 3) (e.g., 40 individuals per community). If I observe these individuals for only 5 years, would that mean that I can only include 5 time varying covariates? Do I run into issues of degrees of freedom if I include 10 time varying covariates? I have read the relevant literature on event history models (e.g., Allison, 1984) and did a thorough search on the internet but can't find any information on this topic. Any information or article/book recommendation on these questions would be highly appreciated! Thanks!