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Re: st: RE: regression: fade rate residual income


From   GBrenner1@gmx.de
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: regression: fade rate residual income
Date   Sun, 26 Oct 2008 23:45:39 +0100

Thank you very much, Mike. This was realy a great help!

Greg B.



-------- Original-Nachricht --------
> Datum: Thu, 23 Oct 2008 21:48:19 -0400
> Von: Michael Hanson <mshanson@mac.com>
> An: statalist@hsphsun2.harvard.edu
> Betreff: Re: st: RE: regression: fade rate residual income

> Greg:
> 
> That explanation is clearer than your earlier messages in terms of  
> what you intend to achieve, but whether your objective makes sense is  
> less clear:  not enough information is provided on that issue.
> 
> The model you (appear to) propose is simply a pooled regression over  
> your panel of firms and years.  Thus,
> 
> reg residual_income L.residual_income
> 
> should give you one single estimate for b1 (the coefficient on lagged  
> residual income) for your entire (unbalanced) sample.  Indeed, for  
> what you have described, -rollreg- (or -rolling-) is exactly *not*  
> what you want to do.  (It can be used to create a time series of  
> cross-sectional estimates of b1 (a different estimate for b1 per  
> year), for example.)
> 
> Some comments, however:
> 
> 1. Any (firm, year) pair that is missing will not be included in the  
> regression.  So Stata already automatically takes care of your  
> concern about missing consecutive observations in computing b1.  This  
> is a consequence of having -xtset- (or, equivalently, -tsset-) your  
> data, so Stata constructs the lagged values correctly.  (You can test  
> this claim by making a copy of your data that only includes (say)  
> even years, then try estimating your model again.  It should fail to  
> produce an estimate, since L.residual_income is undefined for every  
> even-yeared value of residual_income in this synthetic data set.)
> 
> 2. What you call a "fade rate" is probably more generally known as an  
> "autoregressive parameter".  Some textbooks may discuss the "rate of  
> decay" implied by the value of the autoregressive parameter.  The  
> larger is b1, the longer it takes for the effects of any given shock  
> to e(i, t+1) to dissipate from the residual_income variable.  Hence,  
> b1 is also known as a "measure of persistence" of the shocks to e(i, t 
> +1).
> 
> 3. It is not obvious that a pooled OLS estimator for b1 is most  
> appropriate.  As you have a panel data structure, you might as well  
> try to productively exploit it.  I don't know what your exposure to  
> panel data estimators might be, but a large number of textbooks will  
> cover this topic, even at the intermediate/advanced undergraduate  
> level.  (This is particularly true in econometrics, which one might  
> reasonably guess is fairly close to your research area given you have  
> data on firms.)  The basic question to ask yourself in deciding what  
> estimator to use is what do you hypothesize are the properties of  
> your error term, e(i, t+1)?  Once you have some familiarity with some  
> basic panel data estimators, take a look at the -xt- commands for  
> Stata, starting with -xtreg-.
> 
> 4. That said, you have a lagged endogenous regressor in your  
> equation.  Depending on how you model the error term and what your  
> purposes are, that could be a significant problem.  The issues  
> involved with lagged endogenous regressors ("dynamic panel data") are  
> more advanced and only some graduate-level econometrics textbooks  
> cover them.  In Stata 10, see -xtdpd- and related commands for more  
> information.
> 
> Hope this helps,
> Mike
> 
> 
> On Oct 23, 2008, at 5:30 PM, GBrenner1@gmx.de wrote:
> 
> > Dear Nick (statalisters),
> >
> > Thank you for your time. Let me be more clear this time.
> >
> > I would like to examine the autoregressive properties of abnormal  
> > earinings (=residual income) (first order abnormal earnings  
> > autoregression). So I want to use a pooled analysis with one lag,  
> > i.e. residual_income (i, t+1) = b0 + b1 * residual_income(i, t) + e 
> > (i, t+1), where i is a specific company ("name" as identifier) and  
> > t is the year of the observation ("year"). What I want to get is a  
> > fade rate b1 , which describes the reversal of residual_income. b1  
> > should be one single value in order to predict future residual  
> > incomes in another sample ( i.e. residual_income next year equals  
> > b1 times residual income this year). I expect b1 to be about 0.7  
> > (b0=0).
> >
> > When I say the regression should run over every two consecutive  
> > years for a company I mean that the regression should ignore cases,  
> > in which there is more than one year between two observations,  
> > because b1 should be the fade rate of residual_income from one year  
> > to the following year.
> > The identifier for company is "name" and the year is given by  
> > "year". I used:
> >
> > tsset name year
> >
> > .panel variable:  name, 1000 to 270705
> > .time variable:  year, 1974 to 2006, but with gaps
> >
> > rollreg residual_income l.residual_income, move(2) stub(a)
> >
> > .sample may not contain gaps
> >
> > r(198);
> >
> > Well, I don't know whether my idea is an appropiate way to solve  
> > this problem and to get one single b1. Perhaps someone can help me,  
> > whether this is an appropiate way to solve this problem and to get  
> > one single value of b1 and how to get rid of the gaps (because - 
> > rollreg-from SSC does not support gaps in the data).
> >
> > Thanks for your consideration.
> > Greg B.
> >
> >
> > -------- Original-Nachricht --------
> >> Datum: Mon, 20 Oct 2008 13:37:03 +0100
> >> Von: "Nick Cox" <n.j.cox@durham.ac.uk>
> >> An: statalist@hsphsun2.harvard.edu
> >> Betreff: st: RE: regression: fade rate residual income
> >
> >> I think you have problems at various levels.
> >>
> >> The most obvious is that -rollreg- from SSC [please remember to  
> >> explain
> >> where user-written programs you discuss come from] does not  
> >> support data
> >> with gaps. When you -tsset- your data you should have seen a comment
> >> that your data include gaps.
> >>
> >> The next is what you are trying to do. If I read this correctly, you
> >> want to look at regressions for pairs of values within each panel.  
> >> That
> >> gives you at most two distinct data points and you should be able to
> >> solve for the coefficients directly. You will get perfect fits,  
> >> except
> >> when points coincide when regression will be indeterminate. Also,  
> >> there
> >> is no question of an error term.
> >>
> >> On the other hand, I doubt that I am reading you correctly.
> >>
> >> You posted on this topic a week ago. In response both Michael  
> >> Hanson and
> >> I hinted that you may need to explain what you expect in more  
> >> detail to
> >> get better answers.
> >>
> >> Nick
> >> n.j.cox@durham.ac.uk
> >>
> >> GBrenner1@gmx.de
> >>
> >> I would like to run a regression on residual_income. I have yearly
> >> observations of residual income for firms. The year is given in  
> >> variable
> >> "year", the identifier for firm is "name".
> >>
> >> I'd like to run the regression residual_income(year) = b0 + b1 *
> >> residual_income(year-1) + e The regression should run on
> >> "residual_income" over every two consecutive years ("year") within  
> >> each
> >> identifier "name" (whenever there are values for at least two
> >> consecutive years for a given name).
> >>
> >> I used the following:
> >>
> >> drop if missing(residual_income)
> >> tsset name year
> >> rollreg residual_income l.residual_income, move(2) stub(a)
> >>
> >> I hope this command will do what I want but unfortunately Stata  
> >> always
> >> says:
> >> sample may not contain gaps
> >> r(198);
> >>
> >> What might be the problem?
> >>
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