Dear Kit,
Sorry I missed your reply until now. Actually, the code below places
a 1 in days 1381 and 1382 and even if I extend the event period to
twenty days it still drops the dummy variable in the results. I found
a couple papers that recommended this methodology (seemingly unrelated
regression and a dummy for the event days) for regulatory type
events. Further, for similar firms (all of my firms are financial)
with identical event windows this method (using -sureg-) allows one to
reflect the time series and cross-sectional relationship of the errors
in the model but still provide differing loadings on the independent
variables. The "standard" method leaves me with either measuring the
standard deviation of the abnormal returns for the event period across
very few observations if I do it firm by firm or else having to
combine all abnormal returns of the firms in the measurement of an
event abnormal return standard deviation. It is also unclear how to
adjust the errors unless I use something like -pcse-. Most of the
papers utilizing the standard event study approach I reviewed were
able to rely on some kind of randomness in the individual events (such
as earnings or dividend announcements) across firms as opposed to one
event for all firms as well as diversity in the firm type.
1. Is there any reason you would expect the dummy to be dropped for a
longer event window from a -sureg- design viewpoint as opposed to a
statistical measurement viewpoint?
2. I don't understand your comment that -sureg- prevents me from
examining any number of firms
3. Do you have any suggestions or are you familiar with any standard
event study approaches that have similar characteristics to my problem
(short event window, same window for multiple firms, all firms of very
similar error structure - heteroskedastic, strong cross sectional and
time series correlation, etc.)
4. Would you simply use firm specific abnormal return standard
deviations or combine into one abnormal return standard deviation
across all firms?
Thanks for your thoughts and sorry, again, for missing your earlier reply.
Tom
On Tue, Nov 4, 2008 at 6:37 AM, Kit Baum <[email protected]> wrote:
> < >
> If you examine the reshaped data created by this logic, I think you will
> find that only the last observation (with tradedatenum == 1382) has a 1 in
> eventindicator. It is thus a 'singleton dummy'. Putting in a dummy that is 1
> for only 1 observation in a timeseries in plain old OLS is equivalent to
> dropping that observation from the regression, as you can then 'explain'
> y[1382] perfectly -- it has its own intercept term. I don't think you want
> to do this.
>
> This is not the standard methodology for an event study (in particular,
> because the use of -sureg- prevents you from examining any number of firms).
> Why wouldn't you rather want to estimate over the pre-event period and
> forecast returns over the event period, and look for abnormal returns?
>
>
> Kit Baum, Boston College Economics and DIW Berlin
> http://ideas.repec.org/e/pba1.html
> An Introduction to Modern Econometrics Using Stata:
> http://www.stata-press.com/books/imeus.html
>
>
> On Nov 4, 2008, at 02:33 ,Thomas wrote:
>
>>
>> scalar EventBegin = 1381
>> scalar EventEnd = 1382
>> scalar ObservationPeriod = 250
>>
>> keep if TradeDateNum >= EventBegin - ObservationPeriod
>> keep if TradeDateNum <= EventEnd
>>
>> gen EventIndicator = 0
>> replace EventIndicator = 1 if TradeDateNum >= EventBegin
>
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>
--
Thomas Jacobs
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