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From |
"Sandra Virgo" <[email protected]> |

To |
<[email protected]> |

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

Date |
Fri, 15 Nov 2013 16:49:25 +0000 |

Hello all I am using a fractional logit model as my dependent variable is a proportion, specifically the proportion of conceptions ending in maternity. I have two independent variables of interest which are both continuous variables. One is life expectancy, scaled in years. The other is the age-standardised prevalence of long-term limiting illness, which is scaled as a proportion. There are other covariates, both continuous and factor variables. I have found significant relationships between my IVs and the DV, all else equal. I have used the margins command to interpret my findings, but am having trouble interpreting the output. Examples available online tend to use logistic regression rather than fractional logit, so I have had difficulties interpreting output in terms of my own DV. I have computed marginal effects at the mean (MEM), average marginal effects (AME) and marginal effects at representative values (MER). I am aware that getting the marginal effects for a continuous variable can be problematic as it is not a constant estimate. However, in computing MERs I found an interesting 'interaction' with one of my covariates so that is one way of getting around that problem and also a useful exercise. But I am having trouble putting the basic marginal effects into words. The output for my two independent variables is so different and substantively 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 not just a problem with the MEM. Questions: 1) Should I be interpreting the former as "for every one-year increase in life expectancy, the proportion of conceptions ending in maternity increases by .18, with all else held at means" and the latter "for every one-point increase in long-term limiting illness prevalence, the proportion of conceptions ending in maternity decreases 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 a proportional DV? 3) Are the apparent differences in marginal effects between the two variables due to their differences in scaling? 4) If scaling is a problem, should I be standardising the IVs before using a fractional logit and margins? 5) Should I even be trying to compute the marginal effect of a continuous 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/

**Follow-Ups**:**Re: st: interpreting marginal effects of fractional logit with continuous independent variables***From:*Austin Nichols <[email protected]>

**Re: st: interpreting marginal effects of fractional logit with continuous independent variables***From:*David Hoaglin <[email protected]>

**Re: st: interpreting marginal effects of fractional logit with continuous independent variables***From:*Richard Williams <[email protected]>

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