Re: st: Panel data: large number of linear time trends

[ron alfieri <ron.alfieri18@gmail.com>]

Thank you Austin! It seems that the differences are due to my panel
being unbalanced. Using the prior example you can see that both
methods produce different results when dropping some observations to
make the panel unbalanced.

clear all
prog mydetrend, rclass byable(recall)
version 10.1
syntax varlist [if] [in], DETrend(varname)
tempvar eps
marksample touse
regress `varlist' if `touse'
predict double `eps' if e(sample), res
replace `detrend' = `eps' if e(sample)
end

webuse grunfeld
replace invest = . in 4
replace invest = . in 6
replace mvalue = . in 8
replace mvalue = . in 13
replace invest = . in 6
replace invest = . in 7
replace invest = . in 11
replace invest = . in 15
replace invest = . in 21

g i_dtr = .
g mv_dtr = .
by company: mydetrend invest year, det(i_dtr)
by company: mydetrend mvalue year, det(mv_dtr)
areg mv_dtr invest, abs(company)
areg mv_dtr i_dtr, abs(company)
reg mvalue c.invest c.year##i.company


If you can run the interacted version, e.g.
reg mvalue c.invest c.year##i.company
in the link cited, why wouldn't you?

Because I have too many zip codes to include them all as covariates.

Thanks again.

On Wed, May 9, 2012 at 4:43 PM, Austin Nichols <austinnichols@gmail.com> wrote:
> ron alfieri <ron.alfieri18@gmail.com>:
> You don't show what you typed, and it is not clear what you mean by:
> "an interaction between the fixed effect for each zip code and a
> linear time trend"
> --if you mean you interacted a full set of dummies with time, then I
> would expect the same point estimates in both.
>
> Are you neglecting to mention other covariates perhaps?
>
> If you can run the interacted version, e.g.
>  reg mvalue c.invest c.year##i.company
> in the link cited, why wouldn't you?
>
> On Wed, May 9, 2012 at 3:26 PM, ron alfieri <ron.alfieri18@gmail.com> wrote:
>> I am trying to estimate a panel data model with a large number of
>> unit-specific linear time trends (one for each zip code).
>>
>> I am using the method proposed here:
>>
>> http://www.stata.com/statalist/archive/2012-02/msg01108.html
>>
>> Using a subset of my data, I tried using your method and then compared
>> the results to the results from a model where I include zip-code
>> specific time trends by adding as covariates an interaction between
>> the fixed effect for each zip code and a linear time trend.
>>
>> The results are very similar, but not identical.
>>
>> This is how I am interpreting the differences. When de-trending the
>> data for one zip-code at a time your code uses only the data points
>> from that zip code. However, all data points are used when estimating
>> zip-code specific trends by adding as covariates the interactions
>> between the fixed effect for each zip code and a linear trend (with
>> “all data points” I mean even the data points where these interactions
>> take the value of zero that are not used when doing it one zip code at
>> a time).
>>
>> I would appreciate any comments on whether I am interpreting the
>> differences between these two methods correctly. If anyone has an
>> insight on whether one of the methods is more “appropriate” than the
>> other that would be great.
>>
>> Aaron
>
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