I am looking at the repeated cross-sectional data from federal reserves, which has both panel data and repeated cross sectional data at different time-points,e.g. 2007-2009 is a panel while 2010 is a cross sectional data set and everything before that is repeated cross section as well until you get back to the 1983-1989 period which is also a panel. I want to use recent data-sets like 2001 - 2009, of which only the last two years will be true panel data.
RCS data is considered to be inferior to true panel data in general in the sense that in the former case, the same individuals are not followed over time, thus making individual histories unobtainable to include in a model. However, Several authors such as Deaton (1985), Moffitt (1990,1993) showed that the RCS data can be used to estimate a few commonly used models such as the fixed effects model or the linear dynamic model. These methods are based on grouping “similar” individuals in cohorts and the ‘cohort-averages’ are treated as observations from a pseudo-panel. Note that, all the prior studies were conducted on repeated cross-sections without the panel part.
Now, my first question is, 'Is there any known method to compare pseudo-panel data to a panel data?'. My idea is to fit a model to the synthetic panel data and estimate the parameters, then fit the same model to genuine panel data and compare the estimation accuracy. Does this sound correct? Of course, I want ideas about how much of it is doable. (Please note that I have limited ideas about how to manage a huge data-set like the ones available in fedres website.)
Apologies if this was an irrelevant or ill-posed question.