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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? > >> > >> * > >> * 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/ > > > > -- > > "Feel free" - 5 GB Mailbox, 50 FreeSMS/Monat ... > > Jetzt GMX ProMail testen: http://www.gmx.net/de/go/promail > > * > > * 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/ > > * > * 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/ -- Ist Ihr Browser Vista-kompatibel? Jetzt die neuesten Browser-Versionen downloaden: http://www.gmx.net/de/go/browser * * 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/

**References**:**st: regression: fade rate residual income***From:*GBrenner1@gmx.de

**st: RE: regression: fade rate residual income***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**Re: st: RE: regression: fade rate residual income***From:*GBrenner1@gmx.de

**Re: st: RE: regression: fade rate residual income***From:*Michael Hanson <mshanson@mac.com>

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