Announcement

Collapse
No announcement yet.
X
  • Filter
  • Time
  • Show
Clear All
new posts

  • high coeffients in simple OLS and even spline

    I am running a very simple OLSregeession with cross sectional datt having 50companies and 4628 directors' data.My coeffients on one of my intercation erm is comin quite high like 22. The interacion is between a dummy variable and a continuous variable but the continuoys varibale is having many 0 vales in the observatiom. I don't know if such high coeffients is acceptable or not. I am new to stata and so may ne I have asked a very simple qquestion. But any help would be highly appreciated.

  • #2
    sorry that will be 500 companies.

    Comment


    • #3
      Sujata:
      welcome to the list.
      Please note that your chances of getting helpful replies are conditional on posting what you typed and what Stata gave you back (as per FAQ). Thanks.
      Kind regards,
      Carlo
      (StataNow 18.5)

      Comment


      • #4
        Thank you. I am posting my code here. the frst term is an nteraction term whoch is showing me quite high coefficients

        Source | SS df MS Number of obs = 321
        -------------+---------------------------------- F(57, 263) = 3.99
        Model | 2878.28169 57 50.49617 Prob > F = 0.0000
        Residual | 3331.36583 263 12.6667902 R-squared = 0.4635
        -------------+---------------------------------- Adj R-squared = 0.3472
        Total | 6209.64752 320 19.4051485 Root MSE = 3.559

        ----------------------------------------------------------------------------------------
        pb | Coef. Std. Err. t P>|t| [95% Conf. Interval]
        -----------------------+----------------------------------------------------------------
        int_2 | 22.33806 11.96909 1.87 0.063 -1.229379 45.90549
        centered_board_ind_two | -9.565016 7.35646 -1.30 0.195 -24.05007 4.920039
        prom_ceo | -1.066417 .579557 -1.84 0.067 -2.20758 .0747448
        ceo_dual | -.7370154 .5392245 -1.37 0.173 -1.798762 .3247312
        prom_dir_bd | 1.259703 1.546017 0.81 0.416 -1.784443 4.303849
        business_grp_dummy | -.5866 .5135335 -1.14 0.254 -1.59776 .4245603
        ceo_tenure_2013 | -.0001641 .024233 -0.01 0.995 -.0478795 .0475513
        ind_prom_shares | -2.591756 1.023855 -2.53 0.012 -4.607752 -.57576
        lmkt_cpt_prev | 1.320849 .1789101 7.38 0.000 .9685703 1.673127

        Comment


        • #5
          the interaction term int_2 is a combination of one dummy variable and another continuous variable centered_board_ind_two. But this continuous variable is having many '0' values in the observation and I need those 0 values also. The way the coefficient came left me a bit shock d as I am not sure what can be the reason or whether it is good to be it like this.I saw some paper in journal of management studies having quite high coefficient estimates. So I am not sure what to do,
          .

          Comment


          • #6
            Here are the coefficients a little more readably.

            Code:
              |                  int_2    22.33806 |
              | centered_board_ind_two   -9.565016 |
              |               prom_ceo   -1.066417 |
              |               ceo_dual   -.7370154 |
              |            prom_dir_bd    1.259703 |
              |     business_grp_dummy      -.5866 |
              |        ceo_tenure_2013   -.0001641 |
              |        ind_prom_shares   -2.591756 |
              |          lmkt_cpt_prev    1.320849 |
            Such coefficients are slopes or gradients and can't be interpreted without knowing the units and spread of variables. So, what can you tell us about your response (outcome, dependent variable)? Can you show us the results of

            Code:
            summarize pb, detail

            Comment


            • #7
              do you want me to show the detail for each variable?
              I can do that...my outcome variable is pb (price to book ratio)

              Comment


              • #8
                . summarize pb, detail

                PB
                -------------------------------------------------------------
                Percentiles Smallest
                1% .218997 .173974
                5% .403228 .188193
                10% .513983 .192881 Obs 493
                25% .892481 .204482 Sum of Wgt. 493

                50% 1.761988 Mean 3.059937
                Largest Std. Dev. 4.01664
                75% 3.512082 25.0471
                90% 6.668971 28.59596 Variance 16.1334
                95% 9.976144 31.11458 Skewness 4.182878
                99% 24.74946 39.9717 Kurtosis 28.40461

                . summarize int_2, detail

                int_2
                -------------------------------------------------------------
                Percentiles Smallest
                1% -.0094224 -.0094224
                5% -.0094224 -.0094224
                10% -.0094224 -.0094224 Obs 489
                25% -.0094224 -.0094224 Sum of Wgt. 489

                50% 0 Mean -.0002017
                Largest Std. Dev. .0221964
                75% 0 .1334347
                90% 0 .1444237 Variance .0004927
                95% 0 .1572442 Skewness 6.38427
                99% .1334347 .2405775 Kurtosis 50.09779

                . summarize centered_board_ind_two, detail

                centered_board_ind_two
                -------------------------------------------------------------
                Percentiles Smallest
                1% -.0094224 -.0094224
                5% -.0094224 -.0094224
                10% -.0094224 -.0094224 Obs 492
                25% -.0094224 -.0094224 Sum of Wgt. 492

                50% -.0094224 Mean 1.17e-10
                Largest Std. Dev. .0345073
                75% -.0094224 .1723957
                90% -.0094224 .1905776 Variance .0011908
                95% .0905776 .2127998 Skewness 3.882912
                99% .1572442 .2405775 Kurtosis 18.4222

                .


                Comment


                • #9
                  I am sending this for my outcome variable, independent and interaction variables.....

                  Comment


                  • #10
                    Sujata - as you may have seen, your output is very hard to read. If you're going to paste such results, you'll often find a fixed space font like Courier New makes things readable. Also, look at the factor variable notation - it makes handling interactions much easier.

                    If you want to look into this, do predictive margins for the range of your x variables and see if the differences in predictions make sense. While alternatively, with a linear model, you can just multiple the coefficient times the variable's standard error to give you an estimate of the chance in predicted value for a one standard deviation change in the x, this might be a little harder than the margins approach. As Nick pointed out, you have to do this calculation (or the margins equivalent) to say whether the effect sizes make sense.

                    Comment

                    Working...
                    X