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  • #16
    Dear Sebastian,

    Thank for your guide above. Could you show me the way fix random effect not concave, i.e., non-convergence when estimating with xtdpdqml.

    In some case, I cannot get RE estimate due to non-converging although FE is always successful.

    Thank you and regards,

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    • #17
      Did you try the advise that I give in the remarks section of the xtdpdqml help file?
      In the random-effects model, specifying different initial values for the variance parameters with the option initval() may help, in particular increasing the first or second value, or decreasing the third or fourth value such that the following inequality holds for each group:

      b + (a - c * d^2) * T > 0

      where a, b, c, and d refer to the first, second, third, and fourth element specified with the initval() option, respectively. When the option stationary is invoked, this condition
      might not be sufficient to yield feasible initial values. In such a situation, further increasing b can be helpful.

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      • #18
        Dear Sebastian,

        Thank for your reply. I have adjusted (a b c d) as above but it is not successful. Instead, I modified some deep options as such techniques = bfgs, tolerance = 0.001. It converges after 400 iterations! Maybe, default tolerance is too low, say 10E-06. I am not very sure it is a correct way.

        BTW, I guess the default algorithm is NR (Newton-Raphson). Perhaps, BFGS performs better in this case.

        Best,
        Last edited by Binh Pham; 16 May 2017, 12:17.

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        • #19
          Sorry, I have misinterpreted your previous question. My earlier answer is only helpful if the initial values are infeasible.

          Concergence problems are indeed more likely in the random-effects than the fixed-effects model, in particular if you have many regressors. There might be some other peculiarities of your data that add to the complications. You could try to run the estimation with fewer regressors to see if there are any particular variables that cause the convergence problems.

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          • #20
            Dear Sebastian

            Please, It don't know if it is possible to have the Xtsqgmm command with random effect.

            Thanks

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            • #21
              You can use the xtdpdqml command with the option re to estimate a dynamic random-effects model by quasi-maximum likelihood.

              Regarding my GMM command, xtdpdgmm, you can always specify appropriate instruments that would satisfy a random-effects assumption. (Just instrument all exogenous variables by themselves with the iv() option but notice that the lagged dependent variable is always correlated with the unobserved effects by construction. The usual GMM-type instruments need to be employed. The advantage of the xtdpdqml command is that this correlation is built in the likelihood function and no instruments are needed.)

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