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  • Omitted year dummies in xtabond2

    Dear Statalist members,

    I have a problem with some output from xtabond2 (two step sys-gmm)

    When i run my estimations without dummy variables, more than half are omitted. In addition my Sargan and Hansen statistics increase too much (in my opinion).

    Back ground on my research: I am running a dynamic panel model with the aim at assessing the cyclicality of labour compensation growth across Canadian provinces for the period of 1997-2013. Hence, my N=10 and my T=15, which i know is not entirely favourable for this estimation method but my thesis supervisor insists i run this model. Theoretically, non of my variables are endogenous apart from the dependant variable (labour compensation growth), thus this is the only internal instrument i intend to use. Further, i have ran simple OLS, FE and RE estimations, with the hausman test indicating my data fits the RE specification better.

    Please find my out put below (inc year dummies):

    xtabond2 L(0/1).gr_comp_r fisc_def_gdp_r elections gr_gap_r gdp_pc_r union_rate fiscal_rule
    > s yr_1999-yr_2013, artests (3) gmmstyle(L.gr_comp_r, equation(diff) lag(1 2) collapse) ivst
    > yle(gr_gap_r elections fiscal_rules fisc_def_gdp_r gdp_pc_r union_rate, equation(diff)) ivs
    > tyle(yr_1999-yr_2013, equation(level)) robust two
    Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.
    yr_2002 dropped due to collinearity
    Warning: Number of instruments may be large relative to number of observations.
    Warning: Two-step estimated covariance matrix of moments is singular.
    Using a generalized inverse to calculate optimal weighting matrix for two-step estimation.
    Difference-in-Sargan/Hansen statistics may be negative.

    Dynamic panel-data estimation, two-step system GMM
    ------------------------------------------------------------------------------
    Group variable: province Number of obs = 150
    Time variable : year Number of groups = 10
    Number of instruments = 23 Obs per group: min = 15
    Wald chi2(21) = 148342.93 avg = 15.00
    Prob > chi2 = 0.000 max = 15
    --------------------------------------------------------------------------------
    | Corrected
    gr_comp_r | Coef. Std. Err. z P>|z| [95% Conf. Interval]
    ---------------+----------------------------------------------------------------
    gr_comp_r |
    L1. | .0902523 .3070858 0.29 0.769 -.5116248 .6921295
    |
    fisc_def_gdp_r | -.0135247 .0040063 -3.38 0.001 -.021377 -.0056725
    elections | 1.178697 .6221003 1.89 0.058 -.040597 2.397991
    gr_gap_r | -.2798814 .0795031 -3.52 0.000 -.4357046 -.1240582
    gdp_pc_r | -.000209 .0003148 -0.66 0.507 -.000826 .000408
    union_rate | .1323513 .146787 0.90 0.367 -.1553459 .4200485
    fiscal_rules | 0 (omitted)
    yr_1999 | 0 (omitted)
    yr_2000 | 2.160893 3.425384 0.63 0.528 -4.552736 8.874521
    yr_2001 | 0 (omitted)
    yr_2003 | 0 (omitted)
    yr_2004 | 0 (omitted)
    yr_2005 | -.7708339 .8262825 -0.93 0.351 -2.390318 .8486501
    yr_2006 | 0 (omitted)
    yr_2007 | 0 (omitted)
    yr_2008 | 1.988903 2.298823 0.87 0.387 -2.516707 6.494512
    yr_2009 | 0 (omitted)
    yr_2010 | 0 (omitted)
    yr_2011 | 0 (omitted)
    yr_2012 | -.0870417 1.653714 -0.05 0.958 -3.328261 3.154178
    yr_2013 | 0 (omitted)
    _cons | 0 (omitted)
    --------------------------------------------------------------------------------
    Instruments for first differences equation
    Standard
    D.(gr_gap_r elections fiscal_rules fisc_def_gdp_r gdp_pc_r union_rate)
    GMM-type (missing=0, separate instruments for each period unless collapsed)
    L(1/2).L.gr_comp_r collapsed
    Instruments for levels equation
    Standard
    yr_1999 yr_2000 yr_2001 yr_2002 yr_2003 yr_2004 yr_2005 yr_2006 yr_2007
    yr_2008 yr_2009 yr_2010 yr_2011 yr_2012 yr_2013
    _cons
    ------------------------------------------------------------------------------
    Arellano-Bond test for AR(1) in first differences: z = -2.04 Pr > z = 0.041
    Arellano-Bond test for AR(2) in first differences: z = 0.23 Pr > z = 0.817
    Arellano-Bond test for AR(3) in first differences: z = -0.71 Pr > z = 0.475
    ------------------------------------------------------------------------------
    Sargan test of overid. restrictions: chi2(1) = 0.03 Prob > chi2 = 0.860
    (Not robust, but not weakened by many instruments.)
    Hansen test of overid. restrictions: chi2(1) = 0.00 Prob > chi2 = 1.000
    (Robust, but weakened by many instruments.)


    without year dummies:

    . xtabond2 L(0/1).gr_comp_r fisc_def_gdp_r elections gr_gap_r gdp_pc_r union_rate fiscal_rule
    > s, artests (3) gmmstyle(L.gr_comp_r, equation(diff) lag(1 2) collapse) ivstyle(gr_gap_r ele
    > ctions fiscal_rules fisc_def_gdp_r gdp_pc_r union_rate, equation(diff)) robust two
    Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.

    Dynamic panel-data estimation, two-step system GMM
    ------------------------------------------------------------------------------
    Group variable: province Number of obs = 150
    Time variable : year Number of groups = 10
    Number of instruments = 9 Obs per group: min = 15
    Wald chi2(7) = 1522.55 avg = 15.00
    Prob > chi2 = 0.000 max = 15
    --------------------------------------------------------------------------------
    | Corrected
    gr_comp_r | Coef. Std. Err. z P>|z| [95% Conf. Interval]
    ---------------+----------------------------------------------------------------
    gr_comp_r |
    L1. | .2848518 .1807544 1.58 0.115 -.0694203 .6391239
    |
    fisc_def_gdp_r | -.0166521 .0031338 -5.31 0.000 -.0227941 -.01051
    elections | .8123819 .5155152 1.58 0.115 -.1980093 1.822773
    gr_gap_r | -.292718 .0801691 -3.65 0.000 -.4498464 -.1355895
    gdp_pc_r | 4.33e-06 .0001704 0.03 0.980 -.0003296 .0003383
    union_rate | -.0652441 .2687818 -0.24 0.808 -.5920467 .4615585
    fiscal_rules | -4.301556 3.783878 -1.14 0.256 -11.71782 3.114709
    _cons | 9.507943 23.32165 0.41 0.684 -36.20165 55.21754
    --------------------------------------------------------------------------------
    Instruments for first differences equation
    Standard
    D.(gr_gap_r elections fiscal_rules fisc_def_gdp_r gdp_pc_r union_rate)
    GMM-type (missing=0, separate instruments for each period unless collapsed)
    L(1/2).L.gr_comp_r collapsed
    Instruments for levels equation
    Standard
    _cons
    ------------------------------------------------------------------------------
    Arellano-Bond test for AR(1) in first differences: z = -2.22 Pr > z = 0.026
    Arellano-Bond test for AR(2) in first differences: z = 1.34 Pr > z = 0.179
    Arellano-Bond test for AR(3) in first differences: z = -1.30 Pr > z = 0.194
    ------------------------------------------------------------------------------
    Sargan test of overid. restrictions: chi2(1) = 0.64 Prob > chi2 = 0.422
    (Not robust, but not weakened by many instruments.)
    Hansen test of overid. restrictions: chi2(1) = 0.99 Prob > chi2 = 0.321
    (Robust, but weakened by many instruments.)

    From what i understand, my instruments are valid, since the Hansen/Sargan tests cannot be rejected. My SC (1), (2) and (3) also seem quite adequate. I welcome any advice from where i should proceed from here, mainly in relation to the year dummies but also if i have made any fatal errors in my syntax or interpretation.

    ps. i have read some papers where they have reported significant Sargan tests, and others whether the Sargan tests is not significant. From what i have read it should not be <5% (as a rule of thumb) but these papers have me second guessing...clarification on this would also be great!!


    I look forward to any relevant input,

    with appreciation,


    Jordan

  • #2
    Hi Jordan. I am a PhD student currently working with panel data (N=288, T=11) where I have a lagged dependent variable on the right hand side, which steered me towards using GMM and System GMM due to the fact that I have an unbalanced panel.

    I am absolutely not an expert on this estimator, but since I am struggling with it, I have read Roodman's 2009 article over and over. It seems like he stresses that one should always include time dummies in the estimation, when using GMM (either difference or system). He also stresses that N should be sufficiently large (with N=20 being worrisome), but this is something you already know.

    I have a question for you which relates to the way you have written your gmm(...) and iv(...). What is the rationale behind you entering the command equation(diff) as in: gmmstyle(L.gr_comp_r, equation(diff) lag(1 2) collapse) ivstyle(gr_gap_r elections fiscal_rules fisc_def_gdp_r gdp_pc_r union_rate, equation(diff)) ?

    In general, do you know of any good explanations to when it is appropriate for variables to enter transformed equations only and/or levels equation only?

    Best regards and good luck with your research!

    /Hanna Lindström

    Comment


    • #3
      Hello Hanna, well i think you are ahed of me since i am only a master student but I gathered my interpretation from this post:

      https://www.statalist.org/forums/for...m-time-dummies

      see post 7 specifically, as this refers to my situation where all my regressors are exogenous.

      Good luck :D

      Comment


      • #4
        Hi, Yes I saw the reply there, which was very clear about the time dummy case. However, I am still struggling with deciding on the sub options of ivstyle(..eq(diff/level)). Thanks for replying!
        Regards Hanna

        Comment

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