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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Dependent continuous variable with bounded range |

Date |
Tue, 15 Apr 2008 17:49:29 +0100 |

The numeric result for skewness doesn't quite match the fact that the mean is nearer the maximum than the minimum, not that that need that be the case. You possibly have a bit of a tail of fairly lousy firms, but otherwise this distribution looks quite healthy to me. How about gen repute = reputation / 10 xtgee repute ..., link(logit) family(<continuous>) P.S. for "likert" read "Likert" (Rensis Likert, fl.C20) Pavlos C. Symeou Dear Nick, Anders, and Jay, thank you very much for your responses. First, I need to agree with you that this problem can not be handled with interval regression. But as Anders recommends, I need to give further information about my variable in question, "reputation". The variable is a construct consisted of three other variables. These three variables take discrete values that range from 0-10 on a 11-point likert scale, namely 0,1,2,3...,10. These are responses to a survey questionnaire. These three variables' values are first added together and divided by 3 to yield my focal variable "reputation" (unfortunately I only have access to the final variable and not its components). Values for "reputation" closer to 0 suggest lower firm reputation. Values closer to 10 suggest higher firm reputation. A histogram shows a relatively normal distribution as you can see from its Skewness below. I also provide you with statistics on max and min values to respond to Anders' inquiry. Nick advised that I transform my response so that is unbounded using a link function e.g. logit on response/10. (I am not sure how I can do this) Yet, Jay's suggestion is that this might not be that beneficial. Can you please advise on how to proceed? Min 2.95 Max 8.32 Mean 6.267978 Variance .5091705 Skewness -.057921 Kurtosis 3.642905 Yours truly, Pavlos I find both Nick Cox's and Jay Verkuilen's comments very reasonable. But Pavlos does not mention how the variable "reputation" is created. How would reputation get a value of, say, "9.6"? Where does the boundary 0-10 come from, e.g., from the sample's population or from a questionnaire ? Is reputation a scored variable or does it represent original data? Anders Alexandersson Nick Cox outlined several strategies, to which I will add just a bit: (1) How close to the boundaries are your observations? If the distribution looks reasonably symmetric, you probably won't gain much from using a specialized model that "knows" about the boundaries. If you have some skew due to the ceiling or floor but not a truly L- or J-shaped distribution, the logit transformation will probably normalize your errors enough to do ordinary panel regression models. If the distribution is truly L- or J-shaped, no transformation will fix things up. (2) -betafit- (by Nick, Maarten, et al) with clustered robust SEs is a viable alternative that uses the linking strategy. This uses the beta distribution as an error model. It won't adjust for the autoregressive nature of panel data, though, but maybe that'll work well enough. (3) If you want to use the GLM approach and are willing to move to different software (SAS or winBUGS), I can give you examples to do random effects and AR-adjusted beta regression. (One of these days, I want to port a random effects and GEE betareg over to Stata; no time right now.) Jay This does not sound like an interval regression problem to me. Interval regression is for when at least some individual values are known as intervals not points. Here values are known but must lie in a prescribed interval. Divide by 10 and the problem has exactly the same form as that often met for proportions. There are correspondingly various options: 1. Go ahead regardless. The advantage is simplicity. The disadvantage is that there is no guarantee that predicted values will lie in [0,10] certainly for some values of the predictors and possibly for your observed data. Arguably that is quite wrong in principle and it may be a real nuisance for your project. There are implications all the way downstream in terms of assessing fit. 2. Transform your response so that is unbounded. Model in terms of that and then back-transform. 3. A variant of 2, and in many ways preferable, to use a link function e.g. logit on response/10. 4. No doubt others. Nick n.j.cox@durham.ac.uk Anders Alexandersson See -help xtintreg-. It does random-effects interval data regression models. Pavlos C. Symeou <p.symeou@jbs.cam.ac.uk> wrote: > I have panel data for 100 firms for five years and I want to examine the > effects of various variables on "reputation". My variable "reputation" > takes continuous values in the range of 0-10. Namely, it can take > values of 1,2,3 but also of 2.5, 9.6, etc. The values that the variable > "reputation" takes in my sample range between 2.6 - 8.3. Can you please > advise if I can still use panel OLS estimation for panel data or should > I use a different model? In essence, my main concern is the limitations > of the bounded range of my variable. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Dependent continuous variable with bounded range***From:*"Pavlos C. Symeou" <p.symeou@jbs.cam.ac.uk>

**References**:**RE: st: Dependent continuous variable with bounded range***From:*"Pavlos C. Symeou" <p.symeou@jbs.cam.ac.uk>

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