Hello!
I'm writing a thesis on how director reputation changes overtime. Director reputation is derived from market cap of the companies the individual director is holding seats in. I ran both an OLS and a GLS regression and get very different but significant results. I have very limited background in data science and therefore have trouble understanding which model is best suited for my research question. Any help or feedback would be greatly appreciated.
I ran a test for autocorrelation (below) and I tried running the test for heteroskedasticity but I get an error that my stata version only allows up to 800 rows. However to my understanding my data should be homoscedastic.
Below you can find the regression results and I've included an example of my data set.
I'm writing a thesis on how director reputation changes overtime. Director reputation is derived from market cap of the companies the individual director is holding seats in. I ran both an OLS and a GLS regression and get very different but significant results. I have very limited background in data science and therefore have trouble understanding which model is best suited for my research question. Any help or feedback would be greatly appreciated.
I ran a test for autocorrelation (below) and I tried running the test for heteroskedasticity but I get an error that my stata version only allows up to 800 rows. However to my understanding my data should be homoscedastic.
Code:
. xtserial DirRep YearVar
Wooldridge test for autocorrelation in panel data
H0: no first-order autocorrelation
F( 1, 6811) = 3121.669
Prob > F = 0.0000
Code:
. reg DirRep YearVar
Source | SS df MS Number of obs = 50,484
-------------+---------------------------------- F(1, 50482) = 244.00
Model | 157338.357 1 157338.357 Prob > F = 0.0000
Residual | 32552654.4 50,482 644.83686 R-squared = 0.0048
-------------+---------------------------------- Adj R-squared = 0.0048
Total | 32709992.7 50,483 647.940747 Root MSE = 25.394
------------------------------------------------------------------------------
DirRep | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
YearVar | .659354 .0422111 15.62 0.000 .5766199 .7420882
_cons | 60.9483 .2171544 280.67 0.000 60.52267 61.37392
------------------------------------------------------------------------------
Code:
. xtreg DirRep YearVar, re
Random-effects GLS regression Number of obs = 50,484
Group variable: DirectorID Number of groups = 8,035
R-squared: Obs per group:
Within = 0.0016 min = 1
Between = 0.0376 avg = 6.3
Overall = 0.0048 max = 10
Wald chi2(1) = 49.53
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------------------------------------------------------------------
DirRep | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
YearVar | -.0896029 .0127312 -7.04 0.000 -.1145557 -.0646501
_cons | 61.7649 .2896927 213.21 0.000 61.19711 62.33269
-------------+----------------------------------------------------------------
sigma_u | 25.341588
sigma_e | 6.7111548
rho | .93446266 (fraction of variance due to u_i)
------------------------------------------------------------------------------
Code:
* Example generated by -dataex-. For more info, type help dataex clear input double DirectorID byte YearVar int(DirYOB Network) byte(NoNed ExecVar) double DirRep 216931 1 1951 4313 1 0 61 216931 2 1951 4313 1 0 64 216931 3 1951 4313 1 0 62 216931 4 1951 4313 1 0 60 216931 5 1951 4313 1 0 65 722610 1 1939 635 1 0 96 722610 2 1939 635 1 0 97 722610 3 1939 635 1 0 97 722610 4 1939 635 1 0 97 722610 5 1939 635 1 0 97 722610 6 1939 635 1 0 96 722610 7 1939 635 1 0 97 722610 8 1939 635 1 0 96 722610 9 1939 635 1 0 96 722610 10 1939 635 1 0 96 722650 1 1945 2968 1 0 85 722650 2 1945 2968 1 0 82 722650 3 1945 2968 1 0 82 722650 4 1945 2968 1 0 82 722650 5 1945 2968 1 0 83 722650 6 1945 2968 1 0 85 722650 7 1945 2968 1 0 84 722650 8 1945 2968 1 0 85 722650 9 1945 2968 1 0 82 1050210 1 1955 1176 1 0 71 1050210 2 1955 1176 1 0 68 1050210 3 1955 1176 1 0 67 1050210 4 1955 1176 1 0 68 1050210 5 1955 1176 1 0 77 1050210 6 1955 1176 1 0 83 1050210 7 1955 1176 1 0 81 1050210 8 1955 1176 1 0 79 1050210 9 1955 1176 1 0 77 2224030 1 1950 1081 1 1 90 2224030 2 1950 1081 1 1 88 2224030 3 1950 1081 1 1 88 2224030 4 1950 1081 1 1 90 2224030 5 1950 1081 1 1 89 2224030 6 1950 1081 1 1 89 2224030 7 1950 1081 1 1 89 2224030 8 1950 1081 1 1 90 2224030 9 1950 1081 1 1 87 2224030 10 1950 1081 1 1 87 2224190 1 1954 326 1 1 52 2224190 2 1954 326 1 1 52 2224190 3 1954 326 1 1 48 2224190 4 1954 326 1 1 42 2224200 1 1947 204 1 0 52 2224200 2 1947 204 1 0 52 2224200 3 1947 204 1 0 48 2224200 4 1947 204 1 0 42 2224220 1 1951 849 2 1 84 2224220 2 1951 849 2 1 85 2224220 3 1951 849 2 1 85.5 2224220 4 1951 849 2 1 84 2224220 5 1951 849 2 1 84 2224220 6 1951 849 2 1 82 2224220 7 1951 849 2 1 81 2224220 8 1951 849 2 1 74 2224220 9 1951 849 2 1 76 2224220 10 1951 849 2 1 73 2224240 1 1938 0 1 0 70 2224240 2 1938 0 1 0 76 2224240 3 1938 0 1 0 78 2224240 4 1938 0 1 0 77 2224240 5 1938 0 1 0 79 2224240 6 1938 0 1 0 78 2224490 1 1960 1331 1 0 16 2224490 2 1960 1331 1 0 16 2224490 3 1960 1331 1 0 28.000000000000004 2224490 4 1960 1331 1 0 33 2224490 5 1960 1331 1 0 27 2224490 6 1960 1331 1 0 37 2224490 7 1960 1331 1 0 36 2224660 1 1940 152 1 0 54 2224660 2 1940 152 1 0 49 2224660 3 1940 152 1 0 36 2224660 4 1940 152 1 0 50 2224660 5 1940 152 1 0 56.00000000000001 2224660 6 1940 152 1 0 45 2224660 7 1940 152 1 0 46 2224710 1 1959 1119 1 1 90 5354000 1 1953 1405 1 0 23 5354000 2 1953 1405 1 0 15 5354000 3 1953 1405 1 0 10 5354000 4 1953 1405 1 0 5 5354570 1 1965 377 1 0 50 5354570 2 1965 377 1 0 50 5354570 3 1965 377 1 0 53 5354600 1 1959 766 1 1 44 5354600 2 1959 766 1 1 44 5354600 3 1959 766 1 1 47 5354600 4 1959 766 1 1 46 5354600 5 1959 766 1 1 48 10509380 1 1967 699 1 0 65 10509380 2 1967 699 1 0 60 11223500 1 1945 7042 1 0 92 11223500 2 1945 7042 1 0 92 11223500 3 1945 7042 1 0 92 11223500 4 1945 7042 1 0 88 end
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