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.)

  • $\begingroup$ Maybe we can compare two result based on repeated cross section data and panel data using simulation. simulation is performed in different situations, for example when subject specific effects are important or not important and other situations. design of simulating these data must be so attractive! $\endgroup$
    – user82324
    Jul 14 '15 at 20:29

I do not know whether there are established methods to compare panel data to repeated cross-sectional data. But I want to add that true panel data is not always superior to repeated cross-sectional data in general. Attrition or learning effects for example may be a problem in panel data but not in repeated cross-sectional data although I do not know whether these problems are present in your case. But if this is the case, the second and third years (and so on) of your panel data may be problematic compared to repeated cross-sectional data in some sense. You should keep this in mind.

In general I think what you want to do sounds doable and it could reveal new information in comparison with the analysis of cross-sectional data only (although I do not know your research question).

If the estimations differ between both analyses I would have a look whether what could be the reasons by looking at the advantages and disadvantes of both types of datasets. There are several papers about the this topic which might help you such as

Deaton (1985)

Verbeek & Nijman (1992)

Frees (2004)

Lee & Niemeier (1996)

Hsiao (2007)

  • $\begingroup$ Thanks a lot @Arne, this helps. I found the article by Verbeek and Nijman most helpful. Sorry for not stating my research question explicitly. The goal here is to compare the two kinds of data much like what is covered in these articles, we will be applying it on a new kind of data-set, where the comparison might be better. $\endgroup$ Jul 22 '12 at 14:49

In this paper by Seawright (2009) several approaches how to analyze pseudo-panel data are disscussed, including:

Three of the approaches are assesed against a true panel "gold standard" and a pure cross-sectional estimation. The author reaches the conclusion that pseudo-panel matching performs best under the specified conditions due to its robustness against parametric misspecification.


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