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From |
Katie Vigilante <vigsouth@mac.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: question about histograms |

Date |
Mon, 23 Feb 2009 16:51:53 -0500 |

thanks. prof. vigilante On Feb 8, 2009, at 2:33 AM, statalist-digest wrote:

statalist-digest Sunday, February 8 2009 Volume 04 :Number 3330** Send unsubscribe or help commands tomajordomo@hsphsun2.harvard.edu **The digest contains: st: AUTO: Jaai Parasnis is out of the office. [none] st: RE: Re: st: Marginal effect after -clogit- and -xtlogit- st: Re: st: Re: st: prediction in loglinear regression model st: update sheafcoef st: re: prediction in loglinear regression model Re: AW: AW: st: round () if st: data reorganization st: RE: data reorganization Re: st: RE: data reorganization st: Interaction terms in fixed effects analysis st: How to detect the change of i over t? ---------------------------------------------------------------------- Date: Sat, 07 Feb 2009 19:13:57 +1100 From: Jaai Parasnis <Jaai.Parasnis@buseco.monash.edu.au> Subject: st: AUTO: Jaai Parasnis is out of the office.I will be out of the office starting Fri 02/06/2009 and will notreturnuntil Mon 02/23/2009.I am on leave and overseas till Mon 23/02/2009. Please contactFelicityMilne (felicity.milne@buseco.monash.edu.au) in case of any inquires.I willrespond to your message when I return.Note: This is an automated response to your message statalist-digestV4#3329 sent on 7/2/09 6:33:05 PM.You will receive a notification for each message you send to thispersonwhile the person is away. * * 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/ ------------------------------ Date: Sat, 7 Feb 2009 11:29:29 -0500 From: Antonio Silva <asilva100@live.com> Subject: [none] Hello Statlist:I have an OLS model that looks like this: y = constant + b + c + d +e + f.c is the variable in which I am most interested. In the basic model,c turns out NOT to be significant (it is not even close).However, when I include an interaction term in the model, c*f, cturns out to be highly significant.So the new model looks like this: y = constant + b + c + d + e + f +c*f. The interaction term, c*f, is highly significant as well(though in many versions f is NOT significant).My question is this: Is it defensible JUST to report the results ofthe fully specified model--that is, the one with the interaction? Ikind of feel bad knowing that the first model does not produce theresults I desire (I am very happy c ends up significant in the fullmodel--it helps support my hypothesis). I have heard from othersthat if the variable of interest is NOT significant without theinteraction term in the model but IS significant WITH theinteraction term, I should either a) report the results of bothmodels; or b) assume the data are screwy and back away...What do you all think? Thanks so much. Antonio Silva _________________________________________________________________ Windows Live?: Keep your life in sync. http://windowslive.com/howitworks?ocid=TXT_TAGLM_WL_t1_allup_howitworks_022009 * * 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/ ------------------------------ Date: Sat, 7 Feb 2009 12:00:08 -0500 From: "Benjamin Villena Roldan" <bvillena@troi.cc.rochester.edu> Subject: st: RE: Antonio,The correct answer must come from the theoretical considerations ofyourmodel. Do you a have a reasonable argument to justify thisinteraction term?Does it make sense for your theory? Be aware that marginal responseof yourdependent variable with respect to C depends on the level of your F variable. What does it mean? - -----Mensaje original----- De: owner-statalist@hsphsun2.harvard.edu[mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de AntonioSilvaEnviado el: Saturday, February 07, 2009 11:29 AM Para: Stata list Asunto: Hello Statlist:I have an OLS model that looks like this: y = constant + b + c + d +e + f.c is the variable in which I am most interested. In the basic model,c turnsout NOT to be significant (it is not even close).However, when I include an interaction term in the model, c*f, cturns outto be highly significant.So the new model looks like this: y = constant + b + c + d + e + f +c*f.The interaction term, c*f, is highly significant as well (though inmanyversions f is NOT significant).My question is this: Is it defensible JUST to report the results ofthefully specified model--that is, the one with the interaction? I kindof feelbad knowing that the first model does not produce the results Idesire (I amvery happy c ends up significant in the full model--it helps supportmyhypothesis). I have heard from others that if the variable ofinterest isNOT significant without the interaction term in the model but ISsignificantWITH the interaction term, I should either a) report the results ofbothmodels; or b) assume the data are screwy and back away... What do you all think? Thanks so much. Antonio Silva _________________________________________________________________ Windows LiveT: Keep your life in sync. http://windowslive.com/howitworks?ocid=TXT_TAGLM_WL_t1_allup_howitworks_0220 09 * * 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/ ------------------------------ Date: Sat, 7 Feb 2009 12:00:24 -0500 (EST) From: "Supnithadnaporn, Anupit" <gtg065t@mail.gatech.edu> Subject: Re: st: Marginal effect after -clogit- and -xtlogit- Yes, this helps! Thanks Maarten and Steven. Anupit - ----- "Steven Samuels" <sjhsamuels@earthlink.net> wrote:Maarten is correct: -predict(pu0)- gives the predicted probability when the intercept is zero. However, zero may not be a plausible value. For example: consider an unconditional model Y= logit(P) = a + ln(2)X, corresponding to OR = 2.0, for a one-unit increase in X. If the likely range of probabilities is 0.01 to 0.30, the corresponding range of Y is about -4.60 to -0.85. If X > -1, then a = 0 is not possible. -Steve On Feb 6, 2009, at 3:01 PM, Maarten buis wrote:--- On Fri, 6/2/09, Supnithadnaporn, Anupit wrote:I analyze the data using both -clogit- and -xtlogit fe- commands. I would like to get the marginal effect of each independent variables in the model. However, -mfx- command does not work after both -clogit- and -xtlogit fe-, giving the error predict() expression unsuitable for marginal effect calculation r(119); Would anyone please suggest me how to get the marginal effect after running -clogit- and -xtlogit fe-?-mfx, predict(pu0)-* * 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/ ------------------------------ Date: Sat, 7 Feb 2009 12:18:35 -0500 From: Gabi Huiber <ghuiber@gmail.com> Subject: st: Re: This result simply says that the marginal effect of whatever regressor c is the slope of is not a constant. Instead, it depends on the size of whatever regressor f is the slope of. Namely, it's equal to c+c*f*[whatever regressor f is the slope of]. Was your theory suggesting otherwise? If not, pick (a). If yes, why would these particular data say otherwise? Based on the answer to this question you may be right to consider (b), but the other alternative is that the data are fine and your theory's screwy. GabiOn Sat, Feb 7, 2009 at 11:29 AM, Antonio Silva <asilva100@live.com>wrote:Hello Statlist:I have an OLS model that looks like this: y = constant + b + c + d+ e + f.c is the variable in which I am most interested. In the basicmodel, c turns out NOT to be significant (it is not even close).However, when I include an interaction term in the model, c*f, cturns out to be highly significant.So the new model looks like this: y = constant + b + c + d + e + f+ c*f. The interaction term, c*f, is highly significant as well(though in many versions f is NOT significant).My question is this: Is it defensible JUST to report the results ofthe fully specified model--that is, the one with the interaction? Ikind of feel bad knowing that the first model does not produce theresults I desire (I am very happy c ends up significant in the fullmodel--it helps support my hypothesis). I have heard from othersthat if the variable of interest is NOT significant without theinteraction term in the model but IS significant WITH theinteraction term, I should either a) report the results of bothmodels; or b) assume the data are screwy and back away...What do you all think? Thanks so much. Antonio Silva _________________________________________________________________ Windows Live?: Keep your life in sync. http://windowslive.com/howitworks?ocid=TXT_TAGLM_WL_t1_allup_howitworks_022009 * * 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/ ------------------------------ Date: Sat, 7 Feb 2009 18:30:05 +0100 From: "Martin Weiss" <martin.weiss1@gmx.de> Subject: st: Re: <> Stata reserves its most comprehensive postestimation suite of commandsfor -regress- accessible via -help regress_postestimation-. Make useofthem...BTW, are you sure OLS is appropriate for your underlying theoreticalmodel?I grew up with the OLS estimator during my econometrics education,but haveconcluded that it simply does not get you published... HTH Martin _______________________ - ----- Original Message ----- From: "Antonio Silva" <asilva100@live.com> To: "Stata list" <statalist@hsphsun2.harvard.edu> Sent: Saturday, February 07, 2009 5:29 PMHello Statlist:I have an OLS model that looks like this: y = constant + b + c + d+ e +f.c is the variable in which I am most interested. In the basicmodel, cturns out NOT to be significant (it is not even close).However, when I include an interaction term in the model, c*f, cturns outto be highly significant.So the new model looks like this: y = constant + b + c + d + e + f+ c*f.The interaction term, c*f, is highly significant as well (though inmanyversions f is NOT significant).My question is this: Is it defensible JUST to report the results ofthefully specified model--that is, the one with the interaction? Ikind offeel bad knowing that the first model does not produce the results Idesire (I am very happy c ends up significant in the full model--ithelpssupport my hypothesis). I have heard from others that if thevariable ofinterest is NOT significant without the interaction term in themodel butIS significant WITH the interaction term, I should either a) reporttheresults of both models; or b) assume the data are screwy and backaway...What do you all think? Thanks so much. Antonio Silva _________________________________________________________________ Windows LiveT: Keep your life in sync. http://windowslive.com/howitworks?ocid=TXT_TAGLM_WL_t1_allup_howitworks_022009 * * 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/ ------------------------------ Date: Sat, 7 Feb 2009 15:23:50 -0500 From: Kit Baum <baum@bc.edu> Subject: st: prediction in loglinear regression model <> 'LEVPREDICT': module to compute log-linear level predictions without retransformation bias DESCRIPTION/AUTHOR(S) levpredict is a post-estimation command for use after a log-linear regression model has been estimated. It generates predictions of the levels of the dependent variable for the estimation sample. These predictions avoid the retransformation bias that arises when predictions of the log dependent variable are exponentiated. See Cameron and Trivedi, MUS, 2009, 3.6.3. KW: log-linear model KW: regression KW: retransformation bias Requires: Stata version 9.2 Distribution-Date: 20090207 Author: Christopher F Baum, Boston College Support: email baum@bc.edu INSTALLATION FILES (type net install levpredict) levpredict.ado levpredict.hlp--------------------------------------------------------------------------------------------(type -ssc install levpredict- to install) 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 * * 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/ ------------------------------ Date: Sat, 7 Feb 2009 20:58:52 +0000 (GMT) From: Maarten buis <maartenbuis@yahoo.co.uk> Subject: st: update sheafcoefThanks to Kit Baum a update of the -sheafcoef- package is nowavailable from SSC. -sheafcoef- is described below. Martin Weisspointed out an error in the help-file, and -sheafcoef- mis-labeledthe constant when the -eform- option was specified. These problemshave been fixed. To update -sheafcoef- type -ssc install sheafcoef,replace- or -adoupdate-.- -- Maarten - -sheafcoef- is a post-estimation command that estimates sheaf coefficients (Heise 1972). A sheaf coefficient assumes that a block of variables influence the dependent variable through a latent variable. This assumption is not tested, nor is it testable; a sheaf coefficient is just a different way of presenting the results from a model. Its main usefulness is in comparing the relative strength of the influence of several blocks of variables. For example, say we want to know what determines the probability of working non-standard hours (evenings, nights, and weekends) and we have a block of variables representing characteristics of the job and another block of variables representing the family situation of the respondent, and we want to say something about the relative importance of job characteristics versus family situation. In that case one could estimate a logit model with both blocks of variables and optionally some other control variables. After that one can use -sheafcoef- to display the effects of two latent variables, family background and job characteristics, which are both standardized to have a standard deviation of 1, and can thus be more easily compared. - ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room N515 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ - ----------------------------------------- * * 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/ ------------------------------ Date: Sat, 7 Feb 2009 16:39:37 -0500 From: Kit Baum <baum@bc.edu> Subject: st: re: prediction in loglinear regression model <> Maarten Buis pointed out that the term 'loglinear regression model' may be ambiguous, as it is used in some contexts to refer to a type of ANOVA. The routine - -levpredict- works with a standard linear regression model in which the dependent variable is the logarithm of the variable of interest, and does not (necessarily) refer to any sort of ANOVA model. I have updated the help file to make that clear (thanks, Maarten). Kit 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 * * 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/ ------------------------------ Date: 07 Feb 2009 21:43:06 +0000 From: Shehzad Ali <sia500@york.ac.uk> Subject: Re: AW: AW: st: round () if Thanks again, Martin and Jeph. This works perfectly. Shehzad On Feb 6 2009, Martin Weiss wrote:<> You can nest those -cond()-s, see http://www.stata-journal.com/sjpdf.html?articlenum=pr0016 HTH Martin -----Ursprüngliche Nachricht----- Von: owner-statalist@hsphsun2.harvard.edu[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag vonShehzad AliGesendet: Freitag, 6. Februar 2009 17:16 An: statalist@hsphsun2.harvard.edu Betreff: Re: AW: st: round () if Thank you, Jeph and Martin. This was really helpful.To add further, is it possible to add another condition to roundoff weeks0 and <1.5 to become 1 instead of zero while keeping the previouscondition alive? Regards, Shehzad On Feb 6 2009, Martin Weiss wrote:<>Good solution, and in one line. Careful with 1 and 2, though. DoesAlireally want them to become zero? ************* clear* set obs 30 gen weeks=_n gen newweek=cond(abs(round(week,12)-week)<=2,round(week,12),week) list ************* HTH Martin -----Ursprüngliche Nachricht----- Von: owner-statalist@hsphsun2.harvard.edu[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von JephHerrinGesendet: Freitag, 6. Februar 2009 16:56 An: statalist@hsphsun2.harvard.edu Betreff: Re: st: round () if Assuming your week is integers gen newweek=cond(abs(round(week,12)-week)<=2,round(week,12),week) should do. hth, Jeph Shehzad Ali wrote:Hi listers, I want to round a variable 'week' if it is within 2 weeks range ofmultiples of 12 week. So 10 weeks should become 12 weeks while 9weeksshould not change. Similarly 21 weeks would not change while 25weekswill change to 24 weeks. Is there a way to do it in Stata using -round-or other command? Thank you, Shehzad * * 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/ * * 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/ ------------------------------ Date: Sat, 7 Feb 2009 18:07:56 -0500 (EST) From: Arina Viseth <arina@UDel.Edu> Subject: st: data reorganization Dear all, I have a question regarding how to re-organize my data on stata. Here is how the data currently looks: Indice Year 1 1990 1 1991 2 2000 2 2001 2 2003 3 1995 3 1996 4 2002Would it be possible to re-arrange the data so that I would have thefollowing:Indice Year 1 1990, 1991 2 2000, 2001, 2003 3 1995, 1996 4 2002 Any suggestion would be very much appreciated. Thank you in advance. Arina * * 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/ ------------------------------ Date: Sat, 7 Feb 2009 18:44:26 -0500 From: "Steichen, Thomas J." <SteichT@RJRT.com> Subject: st: RE: data reorganizationI can't imagine a reason for wanting this but the following codewill do so (but in new variables).levelsof indice, l(i) qui gen newindice = . qui gen newyear = "" local j = 1 foreach index of local i { levelsof year if indice == `index', l(yrs) s(", ") qui replace newindice = `index' in `j' qui replace newyear = "`yrs'" in `j' local j = `j' + 1 } - ----------------------------------- Thomas J. Steichen steicht@rjrt.com - ----------------------------------- - -----Original Message-----From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Arina VisethSent: Saturday, February 07, 2009 6:08 PM To: statalist@hsphsun2.harvard.edu Subject: st: data reorganization Dear all, I have a question regarding how to re-organize my data on stata. Here is how the data currently looks: Indice Year 1 1990 1 1991 2 2000 2 2001 2 2003 3 1995 3 1996 4 2002Would it be possible to re-arrange the data so that I would have thefollowing:Indice Year 1 1990, 1991 2 2000, 2001, 2003 3 1995, 1996 4 2002 Any suggestion would be very much appreciated. Thank you in advance. Arina * * 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/CONFIDENTIALITY NOTE: This e-mail message, including anyattachment(s), contains information that may be confidential,protected by the attorney-client or other legal privileges, and/orproprietary non-public information. If you are not an intendedrecipient of this message or an authorized assistant to an intendedrecipient, please notify the sender by replying to this message andthen delete it from your system. Use, dissemination, distribution,or reproduction of this message and/or any of its attachments (ifany) by unintended recipients is not authorized and may be unlawful.* * 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/ ------------------------------ Date: Sat, 7 Feb 2009 19:11:44 -0500 (EST) From: Arina Viseth <arina@UDel.Edu> Subject: Re: st: RE: data reorganization Thank you very much! Arina - ---- Original message ----Date: Sat, 7 Feb 2009 18:44:26 -0500From: owner-statalist@hsphsun2.harvard.edu (on behalf of "Steichen,Thomas J." <SteichT@RJRT.com>)Subject: st: RE: data reorganizationTo: "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>I can't imagine a reason for wanting this but the following codewill do so (but in new variables).levelsof indice, l(i) qui gen newindice = . qui gen newyear = "" local j = 1 foreach index of local i { levelsof year if indice == `index', l(yrs) s(", ") qui replace newindice = `index' in `j' qui replace newyear = "`yrs'" in `j' local j = `j' + 1 } ----------------------------------- Thomas J. Steichen steicht@rjrt.com ----------------------------------- -----Original Message-----From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Arina VisethSent: Saturday, February 07, 2009 6:08 PM To: statalist@hsphsun2.harvard.edu Subject: st: data reorganization Dear all, I have a question regarding how to re-organize my data on stata. Here is how the data currently looks: Indice Year 1 1990 1 1991 2 2000 2 2001 2 2003 3 1995 3 1996 4 2002Would it be possible to re-arrange the data so that I would havethe following:Indice Year 1 1990, 1991 2 2000, 2001, 2003 3 1995, 1996 4 2002 Any suggestion would be very much appreciated. Thank you in advance. Arina * * 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/CONFIDENTIALITY NOTE: This e-mail message, including anyattachment(s), contains information that may be confidential,protected by the attorney-client or other legal privileges, and/orproprietary non-public information. If you are not an intendedrecipient of this message or an authorized assistant to an intendedrecipient, please notify the sender by replying to this message andthen delete it from your system. Use, dissemination, distribution,or reproduction of this message and/or any of its attachments (ifany) by unintended recipients is not authorized and may be unlawful.* * 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/ ------------------------------ Date: Sat, 7 Feb 2009 23:44:27 -0500 From: tara.iyer@duke.edu Subject: st: Interaction terms in fixed effects analysis Hi,I'm using panel data to analyze the effect of educationalexpenditure perstudent on test scores across 100 Indian districts over two years.Now, these100 districts belong to three different states. To capture thedifferent effectof expenditure on test scores across the three states, I used the*xi* commandto interact *perstudent* and *state*, with state as the groupvariable. I'musing both OLS and Fixed Effects regression (the OLS regressiontable isbelow). However, I'm not quite sure how to interpret the regression coefficients. I have three specific questions:1. In OLS, is the effect of expenditure in State 2 on *testscore*11.16 unitsmore than State 1? Or is it 11.16 - 0.0068 (-0.0068 is thecoefficient on theinteraction between state 2 and expenditure)2. In Fixed Effects, _Istate_2 and _Istate_3 were dropped._IstaXpers~2 and_IstaXpers~3 were not. How would one interpret the coefficients onIstaXpers~2and _IstaXpers~3 in the Fixed Effects model?3. Does the coefficient on *perstudent* indicate that increasingexpenditureacross *all three states* is decreasing testscores by 0.0025 units?Or does*perstudent* refer to a specific state? Thank you. I really appreciate your taking the time to help. Regards, Tara Iyer . xi: reg testscore i.state*perstudenti.state _Istate_1-3 (naturally coded; _Istate_1omitted)i.state*perst~t _IstaXperst_# (coded as above)Source | SS df MS Number of obs= 230- -------------+------------------------------ F( 5,224) = 22.51Model | 15291.7444 5 3058.34888 Prob > F= 0.0000Residual | 30436.1777 224 135.875793 R-squared= 0.3344- -------------+------------------------------ Adj R-squared = 0.3196Total | 45727.9221 229 199.685249 Root MSE= 11.657-------------------------------------------------------------------------------ner | Coef. Std. Err. t P>|t| [95% Conf.Interval]- -------------+----------------------------------------------------------------_Istate_2 | 11.15724 7.264148 1.54 0.126 -3.15757125.47205_Istate_3 | 10.23105 6.075904 1.68 0.094 -1.74219722.20429perstudent | -.0024748 .0013051 -1.90 0.059 -.0050468 .0000971_IstaXpers~2 | -.0067742 .0022988 -2.95 0.004 -.0113042-.0022441_IstaXpers~3 | -.0020089 .0016681 -1.20 0.230 -.0052961 .0012783_cons | 97.34217 2.719425 35.80 0.000 91.98324102.7011* * 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/ ------------------------------ Date: Sat, 7 Feb 2009 21:46:46 -0700 From: Benson Limann <benli2@live.com> Subject: st: How to detect the change of i over t? Hi all:I am using an unbalanced panel dataset. i is ID and t is time. Every(i,t) has a characteristic x=0 or 1.I want to generate a variable called "become" such that for each(i,t):become=1, if x=0 at time t-1, and x=1 at time t become=0, otherwiseHow to write it? My primary concern is how to deal with the first tfor each i--this observation does not have t-1.Any suggestion would be greatly appreciated. Ben _________________________________________________________________ Windows Live?: E-mail. Chat. Share. Get more ways to connect. http://windowslive.com/howitworks?ocid=TXT_TAGLM_WL_t2_allup_howitworks_022009 * * 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/ ------------------------------ End of statalist-digest V4 #3330 ******************************** * * 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/

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**Follow-Ups**:**Re: st: question about histograms***From:*Kerry Kammire <kkammire@stata.com>

**Re: st: question about histograms***From:*Richard Williams <Richard.A.Williams.5@ND.edu>

**st: RE: question about histograms***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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