[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
"maartenbuis" <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Estimating marginal effects of linear and quadratic term from a logit regression |

Date |
Mon, 08 Nov 2004 10:02:31 -0000 |

Dear Pradeep, I just realised that my answer may have been a bit terse. Let me elaborate a litle: Finding the marginal effect of a variable in a logistic regression is finding the first derivative with respect to that x: {d pr(foreign=1)}/dx. Note that: pr(foreign=1)= exp(xb)/(1+xb) , lets call that L(xb), and xb=_b[-cons]+_b[weight]*weight+_b[price]*price+_b[mpg]*mpg+_b[mpg2] *mpg^2 You can now apply the chain rule {d pr(foreign =1)}/dx = dL/(d xb) * (d xb)/dx. It so happens that L(xb) is the cumulative probality function of the logistic distribution. The deriviative of a cumulative probability function is the probability density function of that distribution. The probability density function of the logistic is: exp(xb)/(1+exp(xb))^2. So: dL/(d xb) = exp(xb)/(1+xb)^2 (d xb)/(d mpg) = _b[mpg] + 2*_b[mpg2]*mpg evaluating this function at the mean for each variable gives you the marginal effect of mpg when all variables have their mean value. Not that you can thus calculate the marginal effect of weight, price and mpg, but not of mpg^2, which makes substantive sense. I hope that this clarifies my earlier post, Maarten --- In statalist@yahoogroups.com, "maartenbuis" <maartenbuis@y...> wrote: > Dear Pradeep, > > Calculating the marginal effect for a logistic regression when one of > the explanatory variables is also entered with an quadratic term can > be done by `hand' (well, by -nlcom- actually) but it gets a bit ugly, > see the example below. > > Notice that the first couple of lines of the -nlcom- is the > probability density function of the logistic: exp(xb)/(1+exp(xb)) ^2. > The last lines of the -nlcom- command is the derivative of xb with > respect to x of interest, in this cas mpg: _b[mpg]+2*_b[mpg2]*(mean > mpg). > > Maarten > > #delim ; > sysuse auto; > > gen mpg2=mpg^2; > > logit foreign price weight mpg mpg2; > > sum price, meanonly; > local price = r(mean); > > sum weight, meanonly; > local weight = r(mean); > > sum mpg, meanonly; > local mpg = r(mean); > > nlcom( > exp(_b[_cons] + _b[price]*`price' + _b[weight]*`weight' + _b > [mpg]*`mpg' + _b[mpg2]*`mpg'^2)/ > (1+exp(_b[_cons] + _b[price]*`price' + _b[weight]*`weight' + _b > [mpg]*`mpg' + _b[mpg2]*`mpg'^2))^2* > (_b[mpg]+2*_b[mpg2]*`mpg') > ); > > --- pradeep.kurukulasuriya@y...> wrote: > > I have a logit model with a linear and quadratic variable along > with some > > other variables (call it stuff). I am trying to estimate the > marginal > > effects for different sets variables in my model where each set is > comprised > > of a linear and quadratic term. Is there a ado file for this that > does not > > require the differentiation to be done manually (and then run a > proceedure > > like nlcom) or is that the best there is? > > > > > * > * 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: Estimating marginal effects of linear and quadratic term from a logit regression***From:*"maartenbuis" <maartenbuis@yahoo.co.uk>

**References**:**Re: st: Estimating marginal effects of linear and quadratic term from a logit regression***From:*"maartenbuis" <maartenbuis@yahoo.co.uk>

- Prev by Date:
**Re: st: Estimating marginal effects of linear and quadratic term from a logit regression** - Next by Date:
**Re: st: Estimating marginal effects of linear and quadratic term from a logit regression** - Previous by thread:
**Re: st: Estimating marginal effects of linear and quadratic term from a logit regression** - Next by thread:
**Re: st: Estimating marginal effects of linear and quadratic term from a logit regression** - Index(es):

© Copyright 1996–2016 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |