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From |
Richard Williams <[email protected]> |

To |
[email protected], <[email protected]> |

Subject |
Re: st: interpreting marginal effects of fractional logit with continuous independent variables |

Date |
Fri, 15 Nov 2013 13:32:55 -0500 |

http://www3.nd.edu/~rwilliam/xsoc73994/Margins02.pdf

http://www3.nd.edu/~rwilliam/xsoc73994/Margins03.pdf

http://www.stata.com/bookstore/regression-models-categorical-dependent-variables/index.html

http://www3.nd.edu/~rwilliam/xsoc73994/Margins01.pptx At 11:49 AM 11/15/2013, Sandra Virgo wrote:

Hello allI am using a fractional logit model as my dependent variable is aproportion, specifically the proportion of conceptions ending in maternity.I have two independent variables of interest which are bothcontinuous variables. One is life expectancy, scaled in years. Theother is the age-standardised prevalence of long-term limitingillness, which is scaled as a proportion. There are othercovariates, both continuous and factor variables. I have foundsignificant relationships between my IVs and the DV, all else equal.I have used the margins command to interpret my findings, but amhaving trouble interpreting the output.Examples available online tend to use logistic regression ratherthan fractional logit, so I have had difficulties interpretingoutput in terms of my own DV.I have computed marginal effects at the mean (MEM), average marginaleffects (AME) and marginal effects at representative values (MER).I am aware that getting the marginal effects for a continuousvariable can be problematic as it is not a constant estimate.However, in computing MERs I found an interesting 'interaction' withone of my covariates so that is one way of getting around thatproblem and also a useful exercise. But I am having trouble puttingthe basic marginal effects into words.The output for my two independent variables is so different andsubstantively strange that I am finding it impossible to interpret:For the life expectancy variable the MEM: ------------------------------------------------------------------------------ | Delta-method| dy/dx Std.Err. z P>|z| [95% Conf. Interval]-------------+----------------------------------------------------------------ple| .0018984 .0007678 2.47 0.013 .0003935 .0034032------------------------------------------------------------------------------ And for the illness prevalence variable the MEM: ------------------------------------------------------------------------------ | Delta-method| dy/dx Std.Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- llti_stand | -.5630636 .0485536 -11.60 0.000 -.658227 -.4679002 ------------------------------------------------------------------------------ For the former it seems the marginal effect is tiny; for the latter enormous.There are similar issues when I compute the AME, so I know it's notjust a problem with the MEM.Questions:1) Should I be interpreting the former as "for every one-yearincrease in life expectancy, the proportion of conceptions ending inmaternity increases by .18, with all else held at means" and thelatter "for every one-point increase in long-term limiting illnessprevalence, the proportion of conceptions ending in maternitydecreases by 56 points, with all else held at means"?The latter cannot be substantively possible.2) Should I therefore be using different language to deal with aproportional DV?3) Are the apparent differences in marginal effects between the twovariables due to their differences in scaling?4) If scaling is a problem, should I be standardising the IVs beforeusing a fractional logit and margins?5) Should I even be trying to compute the marginal effect of acontinuous variable in the first place?Many thanks for your help! Sandra Sandra Virgo PhD Researcher Department of Population Health London School of Hygiene & Tropical Medicine 0207 299 4681 ( tel:02072994681) * * 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/

------------------------------------------- Richard Williams, Notre Dame Dept of Sociology OFFICE: (574)631-6668, (574)631-6463 HOME: (574)289-5227 EMAIL: [email protected] WWW: http://www.nd.edu/~rwilliam * * 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/

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