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From |
Austin Nichols <[email protected]> |

To |
[email protected] |

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

Date |
Mon, 18 Nov 2013 18:15:50 -0500 |

Sandra Virgo <[email protected]> : Is llti_stand measured as a proportion between 0 and 1? If so, you don't want to measure the effect as a one-unit change, but as one hundredth of a unit. On 11/15/13, Sandra Virgo <[email protected]> wrote: > 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 * * 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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