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From |
"Austin Nichols" <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: RE: st: Programming an iterative regression with converging parameters |

Date |
Fri, 15 Aug 2008 06:57:00 -0700 |

Pascal-- I have not tried to understand your entire enterprise, mostly because you gave an incomplete reference and an explanation of your goals that was not immediately clear and an inadequate description of Stata's response to your code [see http://www.stata.com/support/faqs/res/statalist.html#question], but I am guessing that your line while r_`j'-r_`i'>0.001 { is simply never true in your data for some reason so the block of code never executes. Note that such a condition will never be true if the second r is bigger than the first in the first observation in the first iteration, i.e. you are testing whether r_`j' > r_`i' +0.001 in observation 1. If you mean to test whether the distance between two vectors is above some threshold, you want to test whether abs(total(r_`j')-total(r_`i'))>0.001 in essence, or perhaps total(abs(r_`j'-r_`i'))>0.001 which requires first calculating the total or sum over observations. For example: g double s=sum(abs(r`j'-r`i')) while abs(s[_N])>0.001 { ... replace s=sum(abs(r`j'-r`i')) } Is that what you meant? To constrain a parameter b to be strictly positive, simply estimate ln(b) instead, since the most negative ln(b) possible will produce a nonnegative b when you transform it (though if lnb goes below -709 you will get b=0 effectively). But be careful about claiming that "negative returns and rates of growth do not make sense." They do make sense in most fields most of the time, though they may be an unpleasant case to deal with in a theory. My guess is that the code you've shown us is an extremely inefficient way to get where you want to go, but it would be much more productive if you could post a data example starting with -input- and about 5 obs, then illustrate what the desired endpoint is. That is much more likely to fire the imagination of Statalisters. On Fri, Aug 15, 2008 at 4:02 AM, <pascalstock@freenet.de> wrote: > I forgot to ask how to set the constraints that r,g>0 as > negative returns and rates of growth do not make sense. So > far I have > not found a mehtod to set constraints. _________ I am new to STATA and have to replicate a paper by Easton, Taylor, Shroff, Soughiannis (2002) about the implicit calculation of the implied cost of capital r. The problem is that the proxy for the dependent variable X_cT is caluclated with r, while r itself is to be estimated from the regression coefficients. They note that an initial r=0.12 or 12% is used to calculate X_cT. Then it is used as dependent variable to run the first regression. From the resulting intercept and regression coefficient the r and growth rate g are calculated. The new r is used as revised estimate of the implied cost of capital r to recalculate X_cT. The regression is run again and the process of estimating r from the coefficients, and recalculating X_cT starts all over. The logic is simple and straight forward - the programming for a rookie is not. Reading about STATA Programming I guess that a loop using the while command is needed, with the iterative regression to stop when the r from the previous run and the new r are so close as not changing matterially. I used r_`j'-r_`i'>0.0001 as indicator to continue with the loop, as the "true" condition. Also r must be positiv, whereas the forth root can be negative, but is economic meaningless. How to program constraints I do not know - sounds like MATA-rogramming then. So I tried it first without the constraints. Maybe the nl command could work. Here is my do file, and when executing it I even do not get an error message, neither results, not anything: clear set memory 50m use "C:\Users\stock.GESS\Desktop\IBES Datensatz\Data_replication_study_1_X_cT.dta" preserve generate r_1=0.12 generate X_cT_1=x1+x2+x3+x4+[(1+r_1)^3-1]*d0+[(1+r_1)^2-1]*d0+[(1+r_1)-1]*d0 generate depvar=X_cT_1/B0 local i=1 forvalues i=1/100 { local j=1 while r_`j'-r_`i'>0.001 { replace r_`i'=r_`j' replace X_cT_`i'=x1+x2+x3+x4+[(1+r_`i')^3-1]*d0+[(1+r_`i')^2-1]*d0+[(1+r_`i')-1]*d0 replace depvar=X_cT_`i'/B0 regress depvar indvar generate g_`i'=(_b[_cons]+1)^(1/4)-1, replace generate r_`j'=(_b[indvar]+(_b[cons]+1))^(1/4)-1, replace } } save Data_replication_study_1_X_cT_1.dta describe summarize estimates save regression_1, replace estimates tab Any suggestions hw to program such a process are highly appreciated. * * 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**:**RE: RE: st: Programming an iterative regression with converging parameters***From:*pascalstock@freenet.de

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