st: RE: RE: RE: Autocorrelation test for pooled cross section data

[Nick Cox <n.j.cox@durham.ac.uk>]

A minimal but presumably useful check is to calculate some flavour of residuals and plot them against cohort identifier and also space. 

As a non-economist -- and in one very strained sense an ex-economist -- I don't have to sign up to the notion that every check should be matched by a formal test producing a P-value.  

I don't know off-hand how worried you should be relatively about correlation in time and in space for your kind of data, but in principle both could be a concern. 

Nick 
n.j.cox@durham.ac.uk 

Elizabeth Dhuey

I apologize if I don't understand your questions but here is my best shot at
answering them. 

I'm trying to test whether my errors are correlated across time (i.e. across
cohorts). I have a problem because I have 50 different states so I can't use
a simple Durbin-Watson statistic because I could only use that if I had only
one state. 

I only include dummy variables for the states and do not take into account
the location or any spatial data regarding the states. I only allow for an
intercept change for each state. 

Nick Cox

I am unclear on what you want, but as I understand it Durbin-Watson tests
apply only to time series. 

If you have data on 50 U.S. states and are treating them as spatial series,
which of your variables encode information of the location, contiguity,
whatever, of those states? If it is -birthstate- as a categorical variable,
how is that spatial? 

Elizabeth Dhuey

I'm trying to figure out what is the most appropriate test to use is when
working with pooled cross sectional data. In particular, I have aggregated
individual level measures to state level averages for different cohorts from
the U.S. Census. 

I can't use estat dwatson because I have multiple panels and I don't believe
that I can use xtserial because I don't have a true panel.  

I am running the following regression:

xi: reg Y X i.birthstate i.cohort [pw=wgt], robust cluster(birthstate)


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