Statalist


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

st: Re: Suggestions On Event Study Implementation Using Sureg


From   Kit Baum <baum@bc.edu>
To   statalist@hsphsun2.harvard.edu
Subject   st: Re: Suggestions On Event Study Implementation Using Sureg
Date   Fri, 7 Nov 2008 08:10:52 -0500

<>
(1) I do not understand why the dummy is being dropped. Even if it was a singleton dummy it would work (useless, but it would compute). There must be something idiosyncratic about the way you've set up the data. I would look at, say, 3 firms and see if it happens there too with your original setup of a dummy hitting for two days only. In an example like this there is no problem:

webuse grunfeld,clear
reshape wide invest mvalue kstock, i(year) j(company)
g onedum = (year>1953)
forv i=1/10 {
	local eqn "`eqn' (invest`i' mvalue`i' kstock`i' onedum)"
}
sureg `eqn', noh

(2) -sureg- cannot estimate a model in which there are more equations than time periods, or in your case more firms than days. As long as your timespan is long enough, perhaps not a problem.

(3) -sureg- would seem to be an attractive way to look at how an event impacted returns, allowing for he return generating process to differ across firms. As implemented in Stata, though, -sureg- is not a very attractive estimator, as unlike most other Stata commands, it does not allow you to specify the covariance structure (as both -sureg- and - reg3-, on which it depends, are Version 6 commands, that's not too surprising: something about which I have grumbled on numerous occasions). There is no reason why -sureg- (or -reg3-) cannot offer the same array of covariance structures as do other least-squares- based commands, but the fact of the matter is that it does not. So robustifying the estimates is somewhat problematic. If you could, for instance, justify constraining the regressors' coefficients across equations, you could use -xtivreg2- with firm-level clustering, allow for autocorrelation, etc. That would entail calculating a single coefficient for the dummy, rather than firm-specific coefficients as you are trying to do.


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 7, 2008, at 02:33 , Tom wrote:

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?

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/



© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index