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  • alternative for clustered standard errors when having too few clusters

    Hey everyone,

    I have observations on management scores from firms which are nested in countries. This means that observations are clustered.
    In order to account for this clustering I first thought of using clustered standard errors. Unfortunately I only have 18 countries and therefore only 18 clusters which means that using clustered standard errors would cause small sample bias.
    What is an alternative to use in this case??

    I also thought about using a fixed effects model in order to account for the onobserverd heterogeneity, but as my key explanatory variable only varies across countries and not across firms this is not possible.
    So my second question would be if I should do -xtreg,re- or simply -reg- ?

    My model looks as follows: My key explanatory variable is PDI which only varies across countries and not over firms.

    Managementij = a + b1 * PDIj + b2 * xij + eij

    Thanks for any help in advance!!

    Best,
    Hanna


  • #2
    Hanna:
    you may want to try -bootstrap- standard errors.
    Kind regards,
    Carlo
    (Stata 15.1 SE)

    Comment


    • #3
      Hi Carlo,

      thank you for your answer.
      I also thought about using bootstrapped standard errors, I just thought that there might be a special kind (like for e.g. block bootstrap) to use for the above described case.

      Moreover I've got another question:

      I ran the following regression

      Code:
      reg management_mcs pdi_100 i.ownership firm_size_1000 firm_size_sq_1000000
      then I wanted to test for heteroscedasticity and I get two different results by using -estat hettest- and -estat imtest, white-

      Code:
      estat hettest
      
      Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
               Ho: Constant variance
               Variables: fitted values of management_mcs
      
               chi2(1)      =     1.43
               Prob > chi2  =   0.2323
      
      
      
      estat imtest, white
      
      White's test for Ho: homoskedasticity
               against Ha: unrestricted heteroskedasticity
      
               chi2(41)     =     69.87
               Prob > chi2  =    0.0033
      
      Cameron & Trivedi's decomposition of IM-test
      
      ---------------------------------------------------
                    Source |       chi2     df      p
      ---------------------+-----------------------------
        Heteroskedasticity |      69.87     41    0.0033
                  Skewness |      19.14     11    0.0587
                  Kurtosis |       0.46      1    0.4977
      ---------------------+-----------------------------
                     Total |      89.46     53    0.0013
      ---------------------------------------------------

      So my question now is: Which one is the appropriate test to use??


      Best,
      Hanna

      Comment


      • #4
        Hanna:
        as far as your last question is concerned, you can find https://www.statalist.org/forums/for...roskedasticity really helpful.
        Kind regards,
        Carlo
        (Stata 15.1 SE)

        Comment


        • #5
          So I found different techniques which can be used as an alternative for clustered standard errors when there are (too) few clusters:

          - wild cluster bootstrapped t-statistics
          - Block bootstrapped t-statistics
          - cluster-ajusted t-statistics

          unfortunately, non of them does work with a Random Effects model.
          Because my key explanatory variable is time-invariant, I can't use a fixed effects model and need to use a random effects model.

          So my Question is:

          Is there any procedure implemented in STATA as an alternative to the above stated procedures for RE MODELS??

          Best,
          Hanna

          Comment


          • #6
            Hanna:
            I'm not sure I got you right.
            In your original post you shared a -regress- code, that implies one wave of data only.
            Now I see that you are switching to -xtreg-, that implies =>2 waves of data.
            What happened in between the two posts?
            Kind regards,
            Carlo
            (Stata 15.1 SE)

            Comment


            • #7
              Hello Carlo,

              it's not that I have Paneldata in the sense of =>2 waves of data.
              I have "hierarchical" data, which means that observations are on firm level and these firms are nested within countries. Therefore it is also possible to do xtreg..

              in the sense of:
              Code:
              xtset country firm
              and not like:
              Code:
              xtset country time

              Best regards,
              Hanna

              Comment


              • #8
                Hanna:
                if you have a nesting design, the first choice would be -mixed-, which results may overlap with -xtreg, re mle-.
                Kind regards,
                Carlo
                (Stata 15.1 SE)

                Comment

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