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  • Interpretation of marginal effects (Multinomial Logit ) Please help

    Dear Folks,

    My code is like....

    margins, dydx(*) atmeans predict(pr outcome(1))
    margins, dydx(*) atmeans predict(pr outcome(2))
    margins, dydx(*) atmeans predict(pr outcome(3))

    Outcome 3 is my reference category (No turnover), 1 for Forcecd (Turnover) and 2 for (Voluntarily Turnover)

    For a continuous variable
    a 1-unit increase in 3-year EPS volatility results in 2.7 percentage points decrease in the probability of facing forced turnover

    For a dummy variable

    The probability of firms doing forced turnover in basic materials, financials, healthcare and utilities industries is 0.27, 0.25, 0.31 and 0.35 higher respectively than firms in the consumer services group.

    Concerning voluntarily turnover sample, only financial industry has a significant coefficient, so that the choice of financials is associated with a 35 percentage point reduction in the probability of voluntarily turnover.

    Is it still compulsory to mention percentage points or both of these interpretations are right? Are marginal effects relative to the base category like mlogit, or is it related to the category we are discussing?


  • #2
    Michael: Assuming you've estimated a standard MNL model (e.g. mlogit) the marginal effects on the probabilities should not be base-category dependent. There is an adding-up restriction that the marginal effects must obey—for a particular covariate they necessarily sum to zero across all outcomes—but this is not dependent on the base category selected.

    As for percentage points vs. probabilities: Maybe this is a matter of taste, but with probabilities being your outcome of interest then why not express all marginal effects—for continuous or binary covariates—in terms of probability changes?


    • #3
      Thanks a lot sir. Yes, I guess the probability of outcome is a better option.

      What is the different between margins, dydx(*) at means predict(pr outcome(1)) and dydx(*) at means predict(pr outcome(1))

      One quick question concerning marginal effects, did you estimate the marginal effects at the same averages of the explanatory variables?


      • #4
        Andy: I'm not sure I understand your first question ("What is the..."). Can you clarify?

        On your second question, I'm not sure to what estimates you are referring. In any event, as a general matter I do not use the estimate-at-sample-average approach but rather generally take averages of sample estimates ("average partial effects" or "average marginal effects").


        • #5
          Is there any difference in calculating marginal effects at means or not at means

          dydx(*) at means predict(pr outcome(1)) ( I used this code ) or

 according to this paper

          margins, dydx(black) pr(out(3))

          yes, one of my friends replied me this and I did not understand as
          This papers recommends that you do not need to estimate the effects at the same averages of the explanatory variables, (contrary to what I did in the paper)


          • #6
            I think one of them is marginal effect at means (MEM) and other is average marginal effects (AEM)


            • #7
              This might help:


              Also possibly see

              Richard Williams, Notre Dame Dept of Sociology
              Stata Version: 17.0 MP (2 processor)

              EMAIL: rwilliam@ND.Edu