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From |
Maarten buis <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Marginal effects |

Date |
Sat, 22 Mar 2008 12:18:28 +0000 (GMT) |

--- Jos� C. <[email protected]> wrote: > Maarten specifically thinking about the case of Probit - not always > the product of the variables includes a variable dummy. For instance, > as independent variables some models consider: > > y education education(to the square) experience > educationXexperience > > If I use a command as the mfx - the marginal effect just considers > the associated coefficient the education and the correct would be: > > dy/d.education = 1 + 2.education(mean) + experience (mean) - if I use > mfx with medium effects. > > Some statistical packages allow logical (as product of terms) > operations and later the linearization of the equations. In Stata, I > already have to do that creating variables before defining the > equation. To estimate the equation in itself - that doesn't cause any > problem but to establish marginal effects. I easily can calculate the > marginal effects manually starting from the equation but I don't have > s.e. or intervals of the estimates. As I said, I was trying to interpret your question. I assumed you had a dummy because you refered to logical operations (AND, OR, NOT, which result in either true (1) or false (0)) instead of arithmatic operations (addition, multiplication, etc). Notice that the formula you give is the marginal effect of education on the linear predictor (xb) and not on the probability. Below is an example that shows you the marginal effect of education (grade) for average values on all explanatory variables. However, I don't think that it is a good idea to add all kinds of non-linearities to your model and than try to summarize the effect with one number. That defeats the very purpose of adding those non-linearities. For instance, you add interactions because you think that the effect of education is different for people with different amounts of experience. By summarizing the effect of eductation with 1 number you throw all that information away again. *------------- begin example --------------------- sysuse nlsw88, clear gen grade2 = grade^2 gen ttl_expXgrade = ttl_exp*grade probit union grade grade2 ttl_exp ttl_expXgrade sum grade if e(sample) local mgr = r(mean) sum ttl_exp if e(sample) local mttl = r(mean) #delimit ; local xb "_b[_cons] + _b[grade]*`mgr' + _b[grade2]*`mgr'^2 + _b[ttl_exp]*`mttl' + _b[ttl_expXgrade]*`mttl'*`mgr' " ; nlcom normalden(`xb') * (_b[grade] + 2*_b[grade2]*`mgr' + _b[ttl_expXgrade]*`mttl') ; #delimit cr *-------------- end example --------------------- (For more on how to use examples I sent to the Statalist, see http://home.fsw.vu.nl/m.buis/stata/exampleFAQ.html ) Hope this helps, Maarten ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room Z434 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- __________________________________________________________ Sent from Yahoo! Mail. More Ways to Keep in Touch. http://uk.docs.yahoo.com/nowyoucan.html * * 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/

**References**:**st: Marginal effects***From:*Jos� C. <[email protected]>

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