Hi,
I have a datatset cointaining observations from the video game industry. I want to research how software (video game) developers decide in which genre they want to enter with their game. I'm testing following hypotheses with a multinomial logit:
H1: The choice of genre is dependent on the number of existing top-performing games measured by revenue in a genre.
H1a: Game developers try to imitate top performers’ success by entering into the same genre.
H1b: Game developers try to avoid competition with high-selling games by entering into a different genre from top-performing games,
with top performers being those within the top 20% per year according to revenue.
It currently looks like this:
If I'm interpreting the results correctly, top performers do have a significant effect on choice of genre; however, when I want to look at marginal effects, this significance seems to disappear:
Am I doing something wrong or how do I interpret this?
I have a datatset cointaining observations from the video game industry. I want to research how software (video game) developers decide in which genre they want to enter with their game. I'm testing following hypotheses with a multinomial logit:
H1: The choice of genre is dependent on the number of existing top-performing games measured by revenue in a genre.
H1a: Game developers try to imitate top performers’ success by entering into the same genre.
H1b: Game developers try to avoid competition with high-selling games by entering into a different genre from top-performing games,
with top performers being those within the top 20% per year according to revenue.
It currently looks like this:
Code:
Multinomial logistic regression Number of obs = 10,475 LR chi2(126) = 11648.82 Prob > chi2 = 0.0000 Log likelihood = -19497.003 Pseudo R2 = 0.2300 ------------------------------------------------------------------------------------------ sgenre_id3 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------------------+---------------------------------------------------------------- ACTION | (base outcome) -------------------------+---------------------------------------------------------------- ADVENTURE | release_date | .0498489 .0017833 27.95 0.000 .0463537 .053344 ps | -1.103009 .1437939 -7.67 0.000 -1.384839 -.8211778 top_performer_rev_sum | -.5747549 .062992 -9.12 0.000 -.6982169 -.4512928 atari_p | -21.28778 28010.81 -0.00 0.999 -54921.47 54878.89 sega_p | -.5510671 .2650814 -2.08 0.038 -1.070617 -.031517 micro_p | -1.060421 .2115886 -5.01 0.000 -1.475127 -.6457145 nin_p | -1.158782 .1617027 -7.17 0.000 -1.475713 -.8418504 number | -.1286186 .0030603 -42.03 0.000 -.1346167 -.1226206 rev_growth | .5691262 .0739391 7.70 0.000 .4242083 .7140441 _cons | -14.28717 .8208489 -17.41 0.000 -15.896 -12.67833 -------------------------+---------------------------------------------------------------- ARCADE | release_date | .0320185 .003533 9.06 0.000 .0250939 .0389431 ps | -.1778298 .3584196 -0.50 0.620 -.8803193 .5246596 top_performer_rev_sum | .2259194 .1234846 1.83 0.067 -.0161059 .4679447 atari_p | -20.38943 53251.92 -0.00 1.000 -104392.2 104351.5 sega_p | .9951693 .446489 2.23 0.026 .1200668 1.870272 micro_p | .5521793 .599354 0.92 0.357 -.622533 1.726892 nin_p | .6415877 .3419701 1.88 0.061 -.0286614 1.311837 number | -.3190956 .013858 -23.03 0.000 -.3462568 -.2919344 rev_growth | .5903945 .0972139 6.07 0.000 .3998587 .7809303 _cons | -1.782089 1.836259 -0.97 0.332 -5.38109 1.816913 -------------------------+---------------------------------------------------------------- CHILDREN_S_ENTERTAINMENT | release_date | .0150721 .0080371 1.88 0.061 -.0006803 .0308245 ps | .3108431 .6603469 0.47 0.638 -.9834131 1.605099 top_performer_rev_sum | .6036564 .2705252 2.23 0.026 .0734367 1.133876 atari_p | -15.40619 98410.94 -0.00 1.000 -192897.3 192866.5 sega_p | -17.28768 7750.106 -0.00 0.998 -15207.22 15172.64 micro_p | -14.86823 2537.979 -0.01 0.995 -4989.215 4959.478 nin_p | 1.369725 .6131861 2.23 0.025 .1679019 2.571547 number | -.572284 .0578387 -9.89 0.000 -.6856458 -.4589223 rev_growth | .4400669 .1514842 2.91 0.004 .1431633 .7369706 _cons | 8.801733 4.555853 1.93 0.053 -.1275744 17.73104 -------------------------+---------------------------------------------------------------- FAMILY_ENTERTAINMENT | release_date | .0455727 .0017084 26.68 0.000 .0422244 .0489211 ps | -.4294972 .1278267 -3.36 0.001 -.6800329 -.1789615 top_performer_rev_sum | -.126878 .0307522 -4.13 0.000 -.1871513 -.0666048 atari_p | .1598698 1.498964 0.11 0.915 -2.778046 3.097786 sega_p | -.3383123 .2835629 -1.19 0.233 -.8940853 .2174608 micro_p | -.9795381 .1886858 -5.19 0.000 -1.349356 -.6097206 nin_p | -.1098563 .1326103 -0.83 0.407 -.3697677 .1500551 number | -.0674744 .002224 -30.34 0.000 -.0718334 -.0631154 rev_growth | .9722408 .0661211 14.70 0.000 .8426459 1.101836 _cons | -17.21225 .7930431 -21.70 0.000 -18.76658 -15.65791 -------------------------+----------------------------------------------------------------
Code:
. margins, dydx(top_performer_rev_sum ) Average marginal effects Number of obs = 10,475 Model VCE : OIM dy/dx w.r.t. : top_performer_rev_sum 1._predict : Pr(sgenre_id3==ACTION), predict(pr outcome(1)) 2._predict : Pr(sgenre_id3==ADVENTURE), predict(pr outcome(2)) 3._predict : Pr(sgenre_id3==ARCADE), predict(pr outcome(3)) 4._predict : Pr(sgenre_id3==CHILDREN_S_ENTERTAINMENT), predict(pr outcome(4)) 5._predict : Pr(sgenre_id3==FAMILY_ENTERTAINMENT), predict(pr outcome(5)) 6._predict : Pr(sgenre_id3==FIGHTING), predict(pr outcome(6)) 7._predict : Pr(sgenre_id3==FLIGHT), predict(pr outcome(7)) 8._predict : Pr(sgenre_id3==OTHER_GAMES_COMPILATIONS), predict(pr outcome(9)) 9._predict : Pr(sgenre_id3==RACING), predict(pr outcome(10)) 10._predict : Pr(sgenre_id3==ROLE_PLAYING), predict(pr outcome(11)) 11._predict : Pr(sgenre_id3==SHOOTER), predict(pr outcome(12)) 12._predict : Pr(sgenre_id3==SIMULATION), predict(pr outcome(13)) 13._predict : Pr(sgenre_id3==SPORT_GAMES), predict(pr outcome(15)) 14._predict : Pr(sgenre_id3==STRATEGY), predict(pr outcome(16)) 15._predict : Pr(sgenre_id3==17), predict(pr outcome(17)) --------------------------------------------------------------------------------------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] ----------------------+---------------------------------------------------------------- top_performer_rev_sum | _predict | 1 | -.0023884 .001716 -1.39 0.164 -.0057516 .0009749 2 | -.0233723 .2554385 -0.09 0.927 -.5240227 .477278 3 | .0099778 .286822 0.03 0.972 -.552183 .5721386 4 | .0033038 .0760032 0.04 0.965 -.1456598 .1522674 5 | -.0058774 .0144927 -0.41 0.685 -.0342827 .0225278 6 | .0181605 .4047448 0.04 0.964 -.7751247 .8114457 7 | .0006966 .0409284 0.02 0.986 -.0795215 .0809147 8 | -.0387957 1.294957 -0.03 0.976 -2.576866 2.499274 9 | -.0003055 .0085375 -0.04 0.971 -.0170387 .0164277 10 | -.0028814 .0237131 -0.12 0.903 -.0493581 .0435953 11 | -.0065901 .0042347 -1.56 0.120 -.0148899 .0017097 12 | .0328852 .0057612 5.71 0.000 .0215934 .0441769 13 | .0030331 .0182149 0.17 0.868 -.0326675 .0387338 14 | .0015708 .1059767 0.01 0.988 -.2061398 .2092814 15 | .0105829 .0502787 0.21 0.833 -.0879614 .1091273 ---------------------------------------------------------------------------------------
Am I doing something wrong or how do I interpret this?
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