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  • ppml with a large number of fixed effects

    I am trying to estimate a gravity equation (cross section) with a large number of fixed effects. For OLS I use
    xi: areg depvar indepvar, absorb(fixed_effect)
    Is there an equivalent to "absorb" for ppml? The number of fixed effects is very large it will not work with i.fixed_effect.

    Best,
    D

  • #2
    Try xtpoisson with the fe option.

    Best wishes,

    Joao

    Comment


    • #3
      Dear Joao,
      Thank you for the reply.
      I just wanted to clarify one additional detail. I am an applied person and I am not familiar with the technical details of your RESTAT paper. If I use xtpoisson command, should I write that I have used PPML or Poisson?

      Comment


      • #4
        Hi David,

        If you use robust (or clustered) standard errors as you should, you can say that you used PPML (it does not hurt to add a footnote saying that you implemented it by using the xtpoisson command with the FE option).

        Best wishes,

        Joao

        Comment


        • #5
          Thank you very much!

          Comment


          • #6
            Dear Professor,
            If lambda_hat is significant in gravity model such as regression and fixed effect so what is the solution for this problem. Is there any test or any other solution for this problem.

            Thanks so much

            Comment


            • #7
              Sorry, what is lambda_hat?

              Joao

              Comment


              • #8
                First i obtain 0,...,0,lambda_hatit,0,...0: from the following commands.

                forvalues i = 2002 2003 to 2012 {
                quietly probit dummy z1 if year==`i'
                quietly predict xb_hat_`i', xb
                quietly gen lambda_hat_`i' = normalden(xb_hat_`i')/normal(xb_hat_`i') if dummy==1
                quietly gen lambda_hat_d_`i' = lambda_hat_`i'
                quietly replace lambda_hat_d_`i' = 0 if year!=`i'
                quietly drop xb_hat_`i' lambda_hat_`i'
                }

                After this all i run the gravity model such as regression and fixed effect. for Wooldridge, J. M. (1995) sample selection but the probit lambda_hat is negative significant.

                so what is the solution for this problem. Is there any test or any other solution for this problem.

                Thanks so much

                Comment


                • #9
                  Dear Bashir Muhammad

                  My advice is that you should not estimate gravity equations using a sample selection model, just use ppml.

                  Best wishes,

                  Joao

                  Comment


                  • #10
                    Dear Joao Santos Silva,
                    I know but the problem is that my observation are more and cant use ppml because not work

                    Comment


                    • #11
                      Sample size should not be a problem for ppml, it the problem is that you have too many fixed effects, try xtpoisson or ppml_panel_sg.

                      Best wishes,

                      Joao

                      Comment


                      • #12
                        Ok thanks so much

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