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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Reconcile Log Transformed with Untransformed Results |

Date |
Thu, 25 Feb 2010 16:58:36 -0500 |

Erasmo Giambona <e.giambona@gmail.com> : No need to add "in economic terms" as the result is simply not interpretable. To restate my objection from Feb 13: a regression of ln(y+1) on ln(x+1) does not estimate an elasticity, and a change from -0.45 to +0.4 does not correspond to any well-defined percentage point change. If you are unsure of the correct functional form, consider -lpoly- or -fracpoly- or -mkspline- or -pspline- (on SSC). Why not simply estimate a linear regression with OLS and plot your 16 points as well, both with and without the outlier you don't like? sysuse auto, clear keep in 1/16 replace mpg=mpg/20-1 replace weight=weight/3300-1 sc mpg weight ||lfit mpg weight||lfit mpg weight if _n!=13 g y=ln(mpg+1) g x=ln(weight+1) sc y x ||lfit y x||lfit y x if _n!=13, name(why) I can't see why you are ever adding one and taking logs--there is no justification for it that I have seen. On Thu, Feb 25, 2010 at 12:32 PM, Erasmo Giambona <e.giambona@gmail.com> wrote: > Thanks Tony. Actually, I take the log of 1+y. Yes, i tried glm with a > log link and that helps as well. The issue is that i found it > difficult to interpret the results in economic terms. All the details > are in the previous emails. > Erasmo > > On Thu, Feb 25, 2010 at 6:24 PM, Lachenbruch, Peter > <Peter.Lachenbruch@oregonstate.edu> wrote: >> Since one of your y's is negative, -0.03, why should taking logs help? Would a glm with a log link help? >> >> Tony >> >> Peter A. Lachenbruch >> Department of Public Health >> Oregon State University >> Corvallis, OR 97330 >> Phone: 541-737-3832 >> FAX: 541-737-4001 >> >> >> -----Original Message----- >> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Erasmo Giambona >> Sent: Thursday, February 25, 2010 4:32 AM >> To: statalist@hsphsun2.harvard.edu >> Subject: Re: st: Reconcile Log Transformed with Untransformed Results >> >> Thanks Austin. I have been traveling so it has been difficult to look >> into this issue. To answer your question. I am using a two-step >> procedure that is used sometime in monetary policy research. My y is a >> coefficient estimated from a panel regression using firm level data. >> This is the first step. y ranges from -0.03 to +0.07 (with mean=0.023, >> median=0.024, st dev=0.028, skew=-.37, kurt= 2.52). I have 16 y's, one >> per year. In the secon step i regress y on x, where x is an annual >> interest rate spread ranging from -.95% to 1.15% (with mean=3.96e-07, >> median=.0004551, st dev=.6426913, skew=.1102487, kurt= 2.15). The >> scatter of y on x clearly shows that y increase with x, but there is >> one obs (out of the 16) with a very low x and a very high y. I am >> taking the logs to try to reduce the effetc of this obs. Thought this >> is more parimonious relative to the alternative of dropping hte obs >> and winsorizing seems unfeasible with 16 obs. >> >> Any additional thoughts would be appreciated, >> >> Erasmo >> >> On Tue, Feb 16, 2010 at 6:11 PM, Austin Nichols <austinnichols@gmail.com> wrote: >>> Erasmo Giambona <e.giambona@gmail.com>: >>> As I already pointed out, I doubt your estimates correspond to any >>> well-defined percentage point change. Perhaps you can give us a >>> better sense of the distributions of the untransformed y and x (and >>> what they measure and in what units), and what the scatterplot of y >>> against x looks like. You may also prefer to state your effects in >>> terms of standard deviations rather than the interquartile range. >>> >>> On Tue, Feb 16, 2010 at 9:39 AM, Erasmo Giambona <e.giambona@gmail.com> wrote: >>>> Thanks Maarten. In this example, OLS and GLM give very similar >>>> econimic effects. In fact, 74 cents for the OLS is really 9.52% >>>> relative to the mean wage of 7.77. This 9.52% is very much in line >>>> with the 9.7% found with GLM. In my case, the coeff. on X for the OLS >>>> is 0.0064. Relative to the mean for the LHS variable of 0.02. This is >>>> an economic effect of about 28%. With the GLS, using exactly your >>>> code, X gets a coefficient of 2.025 or a 102.5% increase in Y. Or >>>> perhaps, I am misinterpreting this coefficient. >>>> >>>> Thanks, >>>> >>>> Erasmo >>>> >>>> On Mon, Feb 15, 2010 at 9:22 AM, Maarten buis <maartenbuis@yahoo.co.uk> wrote: >>>>> --- On Sun, 14/2/10, Erasmo Giambona wrote: >>>>>> I ran the regressions with both RHS and LHS untransformed >>>>>> using both OLS and GLM with link(log). With the OLS the >>>>>> coeff on X is 0.006 while with the GLM the coefficient is >>>>>> 0.700. I find a bit hard to intepret the GLM coefficient. >>>>> >>>>> Consider the example below: >>>>> >>>>> *--------------- begin example ----------------- >>>>> sysuse nlsw88, clear >>>>> gen byte baseline =1 >>>>> >>>>> reg wage grade >>>>> glm wage grade baseline, /// >>>>> link(log) eform nocons >>>>> *--------------- end example -------------------- >>>>> >>>>> >>>>> The -regress- results are interpreted as follows: >>>>> People without education can expect a wage of >>>>> -1.96 dollars an hour (substantively we know that >>>>> people hardly ever pay for the privelege to work, >>>>> so this is a sign of bad model fit), and they get >>>>> 74 cents an hour more of every additional year of >>>>> education. >>>>> >>>>> The -glm- results are interpreted as follows: >>>>> People without education can expect a wage of >>>>> 2.25 dollars an hour, and for every additional >>>>> year of education they can expect an increase >>>>> of 9.7%. >>>>> >>>>> Hope this helps, >>>>> Maarten * * 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/

**Follow-Ups**:**RE: st: Reconcile Log Transformed with Untransformed Results***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**References**:**Re: st: Reconcile Log Transformed with Untransformed Results***From:*Erasmo Giambona <e.giambona@gmail.com>

**Re: st: Reconcile Log Transformed with Untransformed Results***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**Re: st: Reconcile Log Transformed with Untransformed Results***From:*Erasmo Giambona <e.giambona@gmail.com>

**Re: st: Reconcile Log Transformed with Untransformed Results***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: Reconcile Log Transformed with Untransformed Results***From:*Erasmo Giambona <e.giambona@gmail.com>

**RE: st: Reconcile Log Transformed with Untransformed Results***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

**Re: st: Reconcile Log Transformed with Untransformed Results***From:*Erasmo Giambona <e.giambona@gmail.com>

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