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  • #16
    Carlo Lazzaro Thank you for your answer. I will scrutinize my data more in detail yes. One last question so far: I can't interprete the results from my output above, right? Because all values are insignificant.

    Comment


    • #17
      Domink:
      that's the main issue with your data.
      Such high p-values are sign that there's something wrong with your dataset: hence, any interpretation of your current outcome would be probably misleading.
      Kind regards,
      Carlo
      (Stata 19.0)

      Comment


      • #18
        Carlo Lazzaro Thank you for your answer. In the meantime I fixed that issue


        Code:
        . xtset numeric_country a14y
        repeated time values within panel
        r(451);
        What I did is, I just used idstd as my panel variable.

        Code:
        . xtset idstd a14y
               panel variable:  idstd (unbalanced)
                time variable:  a14y, 2005 to 2019
                        delta:  1 year
        
        . duplicates report
        
        Duplicates in terms of all variables
        
        --------------------------------------
           copies | observations       surplus
        ----------+---------------------------
                1 |       138217             0
        --------------------------------------
        
        . reg l6 i.a14y d3b d3c
        
              Source |       SS           df       MS                                                 Number of obs   =   108,075
        -------------+----------------------------------                                                F(14, 108060)   =      6.73
               Model |  8.8203e+09        14   630024078                                  Prob > F        =    0.0000
            Residual |  1.0123e+13   108,060  93680043.1                             R-squared       =    0.0009
        -------------+----------------------------------                                                Adj R-squared   =    0.0007
               Total |  1.0132e+13   108,074  93749521.6                                    Root MSE        =    9678.8
        
        ------------------------------------------------------------------------------
                  l6 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
        -------------+----------------------------------------------------------------
                a14y |
               2008  |   2.339927   6844.545     0.00   1.000    -13412.87    13417.55
               2009  |    14.0007   6844.768     0.00   0.998    -13401.65    13429.65
               2010  |   15.58416   6844.708     0.00   0.998    -13399.95    13431.11
               2011  |   4.259579   6844.602     0.00   1.000    -13411.07    13419.58
               2012  |  -3.417121   6846.351    -0.00   1.000    -13422.17    13415.33
               2013  |  -3.422353   6844.291    -0.00   1.000    -13418.14    13411.29
               2014  |   3.106308   6844.348     0.00   1.000    -13411.72    13417.93
               2015  |   1241.734   6845.142     0.18   0.856    -12174.65    14658.12
               2016  |   6.352013   6844.883     0.00   0.999    -13409.52    13422.23
               2017  |   7.714403   6845.119     0.00   0.999    -13408.62    13424.05
               2018  |   5.482297   6846.885     0.00   0.999    -13414.32    13425.28
               2019  |  -16.51652   6937.132    -0.00   0.998     -13613.2    13580.17
                     |
                 d3b |   2.944417    2.14347     1.37   0.170    -1.256754    7.145589
                 d3c |  -.0846422   1.360979    -0.06   0.950    -2.752143    2.582858
               _cons |  -3.23e-07   6843.977    -0.00   1.000     -13414.1     13414.1
        ------------------------------------------------------------------------------
        
        . reg l6 a14y d3b d3c
        
              Source |       SS           df       MS                                                 Number of obs   =   108,075
        -------------+----------------------------------                                                F(3, 108071)    =      2.22
               Model |   623177759         3   207725920                                   Prob > F        =    0.0840
            Residual |  1.0131e+13   108,071  93746357.6                             R-squared       =    0.0001
        -------------+----------------------------------                                                Adj R-squared   =    0.0000
               Total |  1.0132e+13   108,074  93749521.6                                   Root MSE        =    9682.3
        
        ------------------------------------------------------------------------------
                  l6 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
        -------------+----------------------------------------------------------------
                a14y |   22.54456   10.71577     2.10   0.035     1.541802    43.54732
                 d3b |   3.167543   2.142326     1.48   0.139    -1.031386    7.366472
                 d3c |   .1852573   1.359148     0.14   0.892    -2.478654    2.849169
               _cons |  -45299.97   21564.98    -2.10   0.036    -87567.03   -3032.915
        ------------------------------------------------------------------------------
        
        . test d3b d3c
        
         ( 1)  d3b = 0
         ( 2)  d3c = 0
        
               F(  2,108071) =    1.10
                    Prob > F =    0.3314

        But do you have any idea what is wrong with the data set?
        Also, now were I have a panel- and a time variable, is there any other way to see the influences of the respetive years?
        Furthermore, is it possible to make the regression only for one specific country?

        Kind regards
        Dominik

        Comment


        • #19
          Dominik:
          1) I cannot say what's wrong with your data, but from -reg l6 i.a14y d3b d3c- outcome it seems like all the time predictors were sample means of samples drawn from a normal distribution (looking at 95% CIs, at least):
          2) I fail to get how you ran -regress- after -xtset-: go -xtreg- if you have panel data;
          3) you can run (panel data) regression model for a single country by imposing a condition via -if- qualifier (see -help if-).
          Kind regards,
          Carlo
          (Stata 19.0)

          Comment


          • #20
            The advice in #3 pointed to the use of xtreg after xtset. The fact that it was not applied points to the need of a thorough review on the subject before fiddling with models. There is no safe shortcut under these terms. One must grasp the core knowledge before going foward with oddities of the analysis. Please read the text as well as the examples presented in the Stata Manual.
            Best regards,

            Marcos

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