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Re: st: Difference-in-Difference on panel data without treatment and control group distinction


From   riyadh shamsan <onlineriyadh@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Difference-in-Difference on panel data without treatment and control group distinction
Date   Thu, 1 Apr 2010 18:44:31 +0100

did you consider Interrupted time series?

On Thu, Apr 1, 2010 at 6:35 PM, Eirik Egeland Nerheim
<Eirik.Nerheim@stud.nhh.no> wrote:
> Thank you!
>
> Eirik
>
>
> -----Opprinnelig melding-----
> Fra: owner-statalist@hsphsun2.harvard.edu på vegne av Nils Braakmann
> Sendt: to 01.04.2010 14:58
> Til: statalist@hsphsun2.harvard.edu
> Emne: Re: st: Difference-in-Difference on panel data without treatment and control group distinction
>
> Dear Eirik,
>
> as far as I see it, you don't really have a DiD-design, not even in
> the pooled OLS case. The crucial point in a DiD is that you have (a)
> two groups that can be distinguished before and and after treatment
> and (b) the treatment hits only one group in the second period. The
> control group is then used to purge common influences of time. Note
> that this only works because you have both time series variation
> (before and after treatment) and cross-sectional variation (treatment
> vs. control group). In your scenario where the financial crisis
> essentially hits everyone, you don't really have a control group (so
> no cross sectional variation), which means that all you are left with
> is the time series variation. Basically, all you can do is looking at
> before-after-comparisons. The usual problem with this approach is that
> you can't really seperate the effect of the treatment from the effect
> of time (or more specifically, the effect of other variables that are
> correlated with time, but are themselves not an outcome of the
> treatment), i.e. the fact that you're looking at observations at two
> different points in time. In fact, I don't see much potential to set
> this up as a DiD-design as there is no group (I can think of) that is
> not affected by the financial crisis. Apart from that, I would use
> fixed-effects and look at within firm(?) comparisons (accepting that
> the claim to causality is quite weak in these estimates). Maybe one
> could have a look at the literature on testing for structural breaks
> to find some inspiration, but this is not really my field of
> experience.
>
> Hope this helped.
> Best
> Nils
>
> On Thu, Apr 1, 2010 at 1:33 PM, Eirik Egeland Nerheim
> <Eirik.Nerheim@stud.nhh.no> wrote:
>> Dear Reader,
>>
>> I am writing a termpaper (MSc level) on financial accounts' effect on
>> equity pricing. My independent variable is market capitalization at
>> close price the market day accounts are released to the public.
>> Explanatory variables are various accounting data such as EBIT, cash
>> flow measures, debt to equity at period end and total assets to control
>> for firm size. All observations are quarterly.
>>
>>
>> I am interested in finding out the difference in coefficient estimates
>> from before till after the financial crisis. With pooled OLS, the
>> difference in difference (DD) estimate is easily obtained and checked by
>> including a dummy that indicates if the observations are before or after
>> the financial crisis was a fact, and an interaction variable (time dummy
>> * explanatory variable):
>>
>>
>> y = a + b * timedummy + c *explanatory variables + d*interaction + u
>>
>>
>> However, I am unsure whether this is the correct approach to use with my
>> panel data. We can assume that all corporations were affected by the
>> financial crisis, so there is no control or treatment group: only
>> observations before and after.
>>
>>
>> In my static panel data model, i.e. only with contemporaneous
>> explanatory variables, I have used economic reasoning and a Hausman test
>> to conclude that a fixed-effects (FE) model is a better model than
>> random-effects (RE). Running the model y = a + b*controls + u with FE,
>> once for observations before the financial crisis and once for those
>> after, I get two  estimates whose difference does not equal the
>> d-paramater if I run the model
>>
>>
>> y = a + b * timedummy + c *explanatory variables + d*interaction + u
>>
>>
>> with FE, which was the case with OLS.
>>
>>
>> Can I still trust the estimate of d when using FE? Should I rather be
>> using another technique to obtain the difference in coefficient
>> estimates?
>>
>>
>> Yours,
>> Eirik E. Nerheim
>>
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