In a 10-year panel with many counties, I'm interested in the effect of a change in a county's robbery rate on a change in the homicide rate, and whether that effect varies in rural versus non-rural counties across 10 years.
I thought the simplest approach would be:
Code:
xtabond D.homicide D.robberies rural Drobberies_rural_interaction a b c y10-y20 if year>2000 , vce(robust)
Specifically, the disappointing output I got from the above is
Code:
note: rural dropped from div() because of collinearity note: Drobberies_rural_interaction dropped from div() because of collinearity note: Drobberies_rural_interaction dropped because of collinearity
I run
Code:
xi: xtabond2 D.homicide D.robberies#rural a b c y10-y20 if year>2000
Code:
invalid interaction specification; the 'D' operator is not allowed with factor variables r(198);
Code:
xi: xtabond2 D.homicide c.robberies#i.rural a b c y10-y20 if year>2000
Code:
Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm. Equation not identified. Regessors outnumber instruments.
Code:
xi: xtabond2 D.homicide c.robberies#i.rural if year>2000
Code:
Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm. Equation not identified. Regessors outnumber instruments.
So my questions are:
1) What is the best way to do this type of interaction in a dynamic panel model? Should I try xtdpdgmm? The syntax for that command looks a little trickier and I worry I might make a mistake.
2) Is it OK to include year fixed effects in an Arellano-Bond model? Or is that already handled by the model, and adding the year fixed effects could create spurious results?
3) Similarly, is it correct to perform an Arellano-Bond model on D.homicide and D.robberies, or would doing so create spurious results? I see from Arellano's paper on this that on the back-end the method does what looks to me like a mean deviation method that first-differences each variable to remove fixed effects. Since I am substantively interested in the CHANGE in homicides and robberies, should I enter those variables as first-differenced as I did in the first code above, and on the back end it will second-difference them? Or in the Arellano-Bond model, should I enter them as not-differenced but just interpret the results as reflecting the effect of a first-differenced change in robberies on a first-differenced change in homicides, without my having to add the "D." to the terms? I haven't seen any papers that plug an already-differenced term in the Arellano-Bond model, so I worry I am violating some aspect of the model...
Thank you for any feedback!
0 Response to Arellano-Bond in Stata: xtabond, xtabond2, or xtdpdgmm if I'm including an interaction? And can I enter differenced focal variables?
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