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  • #16
    Dear all

    After reading a couple of times your posts, I understand what you are saying. However, it is not clear to me whether to follow the p-values of the margins or those from the coefficients in the Original model. I know different hypotheses etc. are beign tested, but just one clear answer which to follow?

    Thank you in advance!

    Comment


    • #17
      As I already stated, my preference is to use the p-values from the original coefficients. I think that is also what is more common. Many analyses do not even present adjusted predictions or marginal effects, but you just about always have the coefficients.

      But I am not the ultimate authority on such things, so you should decide what is best for you given what you want to test.
      -------------------------------------------
      Richard Williams, Notre Dame Dept of Sociology
      Stata Version: 16.0MP (2 processor)

      EMAIL: rwilliam@ND.Edu
      WWW: https://www3.nd.edu/~rwilliam

      Comment


      • #18
        I have encountered a similar problem. My logit models suggests a p value of <.05 and my logit model a p value >.15

        Originally posted by Richard Williams View Post
        As I already stated, my preference is to use the p-values from the original coefficients. I think that is also what is more common.
        I would lean towards presenting the AMEs, because they are easier to interpret, yet I am wrangling with the insignificance.

        Code:
         mi est, post: svy: logit ro i.d_e i.pel  d_pov i.p_bsc i.p_bnvq_s4  i.involv_s4 i.p_sy_s4  c.p_age_sample_s1##c.p_age_sample_s1 i.p_sex_s4
        mimrgns , dydx(i.pek ) cmdmargins predict(pr)
        Code:
          
          Logit coefficients     
        Multiple-imputation estimates Imputations = 30
        Survey: Logistic regression Number of obs = 4,460
        Number of strata = 3 Population size = 4,549.841
        Number of PSUs = 200
        Average RVI = 0.3858
        Largest FMI = 0.9258
        Complete DF = 197
        DF adjustment: Small sample DF: min = 2.31
        avg = 175.31
        max = 195.02
        Model F test: Equal FMI F( 34, 157.1) = 90.89
        Within VCE type: Linearized Prob > F = 0.0000
        ro Coef. Std. Err. t P>t [95% Conf. Interval]
        d_pel
        stage1 1.556983 1.072342 1.45 0.148 -.558007 3.671973
        stage2 -.5627057 .9420055 0.60 0.551 -2.420539 1.295127
        stage3 2.444653 1.197903 2.04 0.043 .0821298 4.807177
        stage4 .3289372 1.40129 0.23 0.815 -2.434705 3.092579
        stage5 -.6252651 1.675261 0.37 0.709 -3.929289 2.678759
        Code: Margins
        dy/dx Std. Err. t P>t [95% Conf. Interval]
        d_pel
        stage1 .1470687 .1426974 1.03 0.304 -.1343723 .4285098
        stage2 -.0241331 .0347124 -0.70 0.488 -.0925933 .0443271
        stage3 .2947183 .218825 1.35 0.180 -.1368519 .7262885
        stage4 .0199796 .0946079 0.21 0.833 -.1666073 .2065665
        stage15 -.0261499 .0563228 -0.46 0.643 -.1372328 .084933
        Note: dy/dx for factor levels is the discrete change from the base level.

        Comment


        • #19
          Clyde Schechter

          Dear Clyde,

          Thank you for your post. Since tests on marginal effect and coefficients are different tests, I can see statistical significance can be different.

          But would it be even possible that coefficients are positive and marginal effect is negative?

          I managed to make a dataset where I get following results.

          1. reg => positive
          2. ivreg => positive
          3. probit coefficient => positive
          4. probit marginal effect => positive
          5. ivprobit coefficient => positive
          6. ivprobit marginal effect => negative

          This is strange in so many levels. #5 and #6 having different sign is strange. #2 and #6 having different sign is strange.

          And if this is possible, in which situation would it occur?

          And if this is possible, shouldn't there be somewhere in the range of x such that ivprobit marginal effect is positive?

          And how should I interpret this? Is X causing increase in Y or decrease in Y? All others are positive & significant. Only marginal effects from ivprobit is negative (and sometimes significant)
          Last edited by Edmondo Ricci; 29 Mar 2019, 00:09.

          Comment


          • #20
            I'm unable to respond to your specific question because I do not use instrumental variables in my work and I have only a very limited understanding of how they work.

            I can say that with linear models, the marginal effect is equal to the coefficient. With non-linear models, the marginal effect must be conditioned on particular values of the predictor variables (or averaged over the distribution of the predictor variables) and can vary considerably, whereas regression coefficients are unconditional. So there is no necessary type of agreement between a regression coefficient and all of the infinitely many marginal effects associated with that variable.

            Comment


            • #21
              Clyde Schechter

              Dear Clyde,

              Thank you again

              Comment


              • #22
                Following this thread, I am still confused as to why the significance levels of the ivprobit original coefficients and those of its average marginal effects should not be identical. I ran an ivprobit which returned positive and significant coefficients (varying p-values for the different specifications), however the corresponding average marginal effects after running margins, dydx(*) predict (pr) were all positive and insignificant (p>0.1 for all specifications).
                The other thing is the command 'margins, dydx (*) predict (pr)' returns the average marginal effects of the instrumental variable as well, ideally this should not happen..any idea why this is the case?
                Thanks in advance.

                Comment


                • #23
                  Teresa, welcome to Statalist.

                  i suggest reading the Statalist FAQ, esp. point 12 about asking Qs effectively. Showing exactly what you typed and how Stata responded can make it easier to see what you are talking about.

                  Also, while I might add a link to old threads, I like to start a new thread rather than add on to a long older one. If something has 20+ posts I usually don’t want to go to the trouble of getting up to date on what has already been talked about.

                  i personally do not get too surprised about difference in significance levels between coefficients and marginal effects. Marginal effects can be computed in many ways, e.g. atmeans, asobserved, or at values chosen by the user. These different ways might produce different significance levels. I usually just focus on the significance of the coefficients.
                  -------------------------------------------
                  Richard Williams, Notre Dame Dept of Sociology
                  Stata Version: 16.0MP (2 processor)

                  EMAIL: rwilliam@ND.Edu
                  WWW: https://www3.nd.edu/~rwilliam

                  Comment

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