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  • Boldeanu Teodor
    replied
    There are not two models. Only one. But because some of my indep variables were non-stat., I choose to use the first difference. Afterwards I wrote the two commands in stata to see the results. The first one is with FD and the second is the one in which the variables are not differenced. This is why I asked you if your command already does the FD, because if it does than it means that i will compute a Diff of FD model which i don t want to do. Some authors state that if you have non-stat variables in your model, the best way is to continue with a FD methodology.

    Also in the help - you have the sta - assume stationarity of all variables.

    Thank you for your answer and help.

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  • Sebastian Kripfganz
    replied
    I have to give the typical answer of an economist: It depends.

    In your first equation you are specifying a random-effects model in first differences. It is thus implicitly assumed that there was a linear random trend in the levels model since the trend becomes an intercept after differencing. In contrast, your second equation assumes a model with a random intercept (instead of a random trend) in levels.

    Secondly, for the estimation it is assumed that the error term of the specified equation does not exhibit serial correlation. Thus, with your first equation you are assuming that there is no serial correlation in the first-differenced errors while in your second equation your are assuming absence of serial correlation in the levels errors.

    With a dynamic model, non-stationarity of your observed variables should not be much of a concern. The relevant questions that you need to answer for your model choice are the two preceding ones about the unobserved model components. There is no general answer which of the two specifications should be preferred in your case.

    Leave a comment:


  • Boldeanu Teodor
    replied
    Can I apply the command for a model in first difference. I have some non-stationary variables and to obtain stationarity I applied FD. xtdpdqml already does the FD (you said on your site that the fixed effect one is in FD).

    xtdpdqml D.y1 D.x5 D.x6 D.x9 D.x10 D.x13 D.x15 D.x16 D.x17 D.x20 D.x23 D.x24 D.x26 D.x28 D.x33, re
    xtdpdqml y1 x5 x6 x9 x10 x13 x15 x16 x17 x20 x23 x24 x26 x28 x33, re

    Thank you!

    Leave a comment:


  • Sebastian Kripfganz
    replied
    A new update is available with some bug fixes, in particular regarding the sample determination in the case of unbalanced panels with interior gaps. The available postestimation features have also been improved. In particular, it is now possible to use the suest command for a generalized Hausman specification test after having obtained fixed-effects and random-effects estimates with the xtdpdqml command. Here is an example:
    Code:
    webuse abdata
    
    xtdpdqml n w k yr1978-yr1984, mlparams
    estimates store fe
    
    quietly xtdpdqml n w k yr1978-yr1984, re mlparams
    estimates store re
    
    suest fe re, vce(cluster id)
    test ([fe__model]LD.n = [re__model]L.n) ([fe__model]D.w = [re__model]w) ([fe__model]D.k = [re__model]k)
    The latest version of the xtdpdqml command now requires at least Stata version 12.1.

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  • Sebastian Kripfganz
    replied
    The latest update of the xtdpdqml command now also computes robust standard errors with the vce(robust) option for all model specifications. The robust VCE is computed along the usual ML lines with the sandwich formula.

    For the fixed-effects model, Hayakawa and Pesaran (2015) have shown that the transformed likelihood estimator is still consistent in the case of cross-sectional heteroskedasticity, and the robust VCE is a consistent estimator of the asymptotic variance-covariance matrix in this case:
    • Hayakawa, K., and M. H. Pesaran (2015). Robust standard errors in transformed likelihood estimation of dynamic panel data models with cross-sectional heteroskedasticity. Journal of Econometrics 188 (1), 111-134.

    Leave a comment:


  • Sebastian Kripfganz
    replied
    I just released an update of the xtdpdqml package with significant improvements, in particular regarding the random-effects estimation.

    The new version now has analytical gradients and Hessian matrices implemented for all model versions which yields large improvements in computational speed. Their implementation also helped to identify a mistake in the specification of the random-effects log-likelihood function that is corrected now. (Estimates will be different compared to previous versions.)

    For the random-effects model, the option noeffects has been added. If specified, the variance of the unobserved unit-specific effects is constrained to be zero which consequently yields identical coefficient estimates as with regress.

    The Stata help file now also contains some guidelines in the Remarks sections on how to deal with initial values that are not feasible for the maximization.

    For a fresh installation of the package type:
    Code:
    net install xtdpdqml, from(http://www.kripfganz.de/stata/)
    To update an existing installation type:
    Code:
    adoupdate xtdpdqml, update
    An updated background note is available on my website: http://www.kripfganz.de/stata/
    Last edited by Sebastian Kripfganz; 07 Jun 2015, 07:22.

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