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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: OLS assumptions not met: transformation, gls, or glm as solutions? |

Date |
Mon, 17 Dec 2012 18:46:46 +0000 |

GLM can mean the "general linear model" or "generalised linear models", which are not identical sets. Some statistical software has commands for the former -- arguably in Stata -regress- is general enough for one to be unnecessary -- but In a Stata context -glm- (with -cmdname- notation) means the generalized linear model command of that, for which conditional normal distribution is but one allowed flavour for error behaviour; other distribution families are well supported. Some of those families will be heteroscedastic (e.g. the Poisson). I don't know that any "research" is needed here. Reading the documentation will suffice. Nick On Mon, Dec 17, 2012 at 5:32 PM, Laura R. <laura.roh@googlemail.com> wrote: > Thank you all for your help. I am still a bit confused, because now I > read that also with GLM homoscedasticity and normality of residuals > are assumptions that have to be met. But I will research further on > that type of models in order to find out whether this works better in > my case than OLS. > > Laura > > > > 2012/12/17 Ryan Kessler <ryan.kessler.stata@gmail.com>: >> The User's Guide is a great place to start. Maarten's point can also >> be illustrated via simulation: >> >> capture program drop ols_sim >> program define ols_sim, rclass >> version 12 >> syntax [, NONCONstant robust] >> set obs 300 >> tempvar y x >> gen `x'=1 in 1/100 >> replace `x'=2 in 101/200 >> replace `x'=3 in 201/300 >> >> if "`nonconstant'"!="" gen `y'=rnormal(`x',`x'^2) in 1/300 >> else gen `y'=rnormal(`x',1) in 1/300 >> >> reg `y' `x', `robust' >> return scalar beta1=_b[`x'] >> test `x'=1 >> return scalar pv=r(p) >> end >> >> clear >> local reps=1000 >> cii `reps' `reps'*0.05 >> local v_lb=round(r(lb), 0.001) >> local v_ub=round(r(ub), 0.001) >> >> simulate beta=r(beta1) pv=r(pv), reps(`reps'): ols_sim, nonconstant robust >> qui count if pv <= 0.05 >> local rej_rate=`=r(N)'/`reps' >> di "Rejection rate =`rej_rate' [`v_lb',`v_ub']" >> >> Hope this helps! >> >> Ryan >> >> On Mon, Dec 17, 2012 at 10:27 AM, Maarten Buis <maartenlbuis@gmail.com> wrote: >>> On Mon, Dec 17, 2012 at 4:17 PM, Carlo Lazzaro wrote: >>>> The main meaning of my example is that you cannot be sure, after invoking >>>> -robust-, that heteroskedasticity is automatically removed. In other words, >>>> homoskedasticity should be checked graphically even after - robust -. >>> >>> Robust standard errors do not change the coefficients, just the >>> standard errors change. So the predicted values and residuals will >>> also remain unchanged after you have specified the -vce(robust)- >>> option. The whole point of robust standard errors is not that it >>> "solves" in some way for heteroskedasticity, it just makes that >>> "assumption" irrelevant. For more, see section 20.20 of the User's >>> Guide. >>> * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: OLS assumptions not met: transformation, gls, or glm as solutions?***From:*"Laura R." <laura.roh@googlemail.com>

**Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?***From:*"Laura R." <laura.roh@googlemail.com>

**R: st: OLS assumptions not met: transformation, gls, or glm as solutions?***From:*"Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it>

**Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?***From:*Ryan Kessler <ryan.kessler.stata@gmail.com>

**Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?***From:*"Laura R." <laura.roh@googlemail.com>

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