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RE: st: Interpretation of margins in the presence of fixed effects
From
Dana Shills <[email protected]>
To
"[email protected]" <[email protected]>
Subject
RE: st: Interpretation of margins in the presence of fixed effects
Date
Fri, 6 Sep 2013 09:45:38 -0400
Hi Jed:
I understand the role of the constant but I am trying to figure what exactly the margins command is estimating in the presence of other dummies. So the model without the constant is the first one below. How do these age coefficients compare to the predictive margins in the model below that??
Thanks
Dana
. reg size ages1-ages9 i.inum, noconstant
Source | SS df MS Number of obs = 97
-------------+------------------------------ F( 39, 58) = 3.34
Model | 2853694.83 39 73171.6624 Prob> F = 0.0000
Residual | 1269729.17 58 21891.8822 R-squared = 0.6921
-------------+------------------------------ Adj R-squared = 0.4850
Total | 4123424 97 42509.5258 Root MSE = 147.96
------------------------------------------------------------------------------
size | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ages1 | 134 147.9591 0.91 0.369 -162.1722 430.1722
ages2 | 319.1145 161.2046 1.98 0.053 -3.571458 641.8005
ages3 | 239.8429 165.3636 1.45 0.152 -91.16828 570.8542
ages4 | 208.4776 162.3196 1.28 0.204 -116.4403 533.3955
ages5 | 305.1386 168.829 1.81 0.076 -32.80939 643.0865
ages6 | 353.9226 171.9741 2.06 0.044 9.679101 698.1661
ages7 | 157.7671 170.1343 0.93 0.358 -182.7938 498.3279
ages8 | 611.1547 176.773 3.46 0.001 257.305 965.0044
ages9 | 729.833 196.4746 3.71 0.000 336.5464 1123.12
|
inum |
2 | -234.5949 168.2358 -1.39 0.169 -571.3555 102.1657
3 | -17.52719 160.0994 -0.11 0.913 -338.0009 302.9465
4 | -55.42242 169.1826 -0.33 0.744 -394.0781 283.2333
5 | -271.0341 187.8332 -1.44 0.154 -647.0231 104.955
6 | -118.9656 166.5016 -0.71 0.478 -452.2547 214.3236
7 | -95.84294 221.8941 -0.43 0.667 -540.0123 348.3264
8 | -206.4776 219.635 -0.94 0.351 -646.1248 233.1696
9 | -234.8429 195.681 -1.20 0.235 -626.5411 156.8552
10 | -83.4776 219.635 -0.38 0.705 -523.1248 356.1696
11 | -451.0993 188.756 -2.39 0.020 -828.9356 -73.26306
12 | -271.1145 218.8122 -1.24 0.220 -709.1148 166.8857
13 | -68.56328 168.2744 -0.41 0.685 -405.4011 268.2746
14 | -55.88331 171.9721 -0.32 0.746 -400.1229 288.3562
15 | -265.9787 191.091 -1.39 0.169 -648.489 116.5315
16 | -125.1974 175.2724 -0.71 0.478 -476.0432 225.6484
17 | -147.8429 221.8941 -0.67 0.508 -592.0123 296.3264
18 | -148.5633 168.2744 -0.88 0.381 -485.4011 188.2746
19 | -181.3566 181.0713 -1.00 0.321 -543.8101 181.097
20 | -297.1145 218.8122 -1.36 0.180 -735.1148 140.8857
21 | -13.84294 221.8941 -0.06 0.950 -458.0123 430.3264
22 | -193.3253 167.3785 -1.16 0.253 -528.3697 141.7191
23 | -160.5306 193.9322 -0.83 0.411 -548.7281 227.6669
24 | -145.7978 165.7959 -0.88 0.383 -477.6744 186.0788
25 | -109.5 181.2121 -0.60 0.548 -472.2354 253.2354
26 | -290.1386 224.4886 -1.29 0.201 -739.5012 159.2241
27 | -183.1603 191.4903 -0.96 0.343 -566.4698 200.1492
28 | -77.4776 219.635 -0.35 0.726 -517.1248 362.1696
29 | -120.0795 177.5111 -0.68 0.501 -475.4067 235.2476
30 | -346.9226 226.8633 -1.53 0.132 -801.0388 107.1937
31 | -204.4776 219.635 -0.93 0.356 -644.1248 235.1696
------------------------------------------------------------------------------
>> Case II: WITH INDUSTRY DUMMIES
>>
>> . reg size i.agedum i.inum
>>
>> Source | SS df MS Number of obs = 97
>> -------------+------------------------------ F( 38, 58) = 1.67
>> Model | 1385935.82 38 36471.9953 Prob> F = 0.0390
>> Residual | 1269729.17 58 21891.8822 R-squared = 0.5219
>> -------------+------------------------------ Adj R-squared = 0.2086
>> Total | 2655664.99 96 27663.177 Root MSE = 147.96
>>
>> ------------------------------------------------------------------------------
>> size | Coef. Std. Err. t P>|t| [95% Conf. Interval]
>> -------------+----------------------------------------------------------------
>> agedum |
>> 2 | 185.1145 63.99243 2.89 0.005 57.01978 313.2093
>> 3 | 105.8429 73.84605 1.43 0.157 -41.97599 253.6619
>> 4 | 74.4776 66.75151 1.12 0.269 -59.14007 208.0953
>> 5 | 171.1386 81.31018 2.10 0.040 8.378543 333.8986
>> 6 | 219.9226 87.65383 2.51 0.015 44.46437 395.3808
>> 7 | 23.76708 83.98693 0.28 0.778 -144.351 191.8852
>> 8 | 477.1547 96.73068 4.93 0.000 283.5272 670.7822
>> 9 | 595.833 129.2686 4.61 0.000 337.0737 854.5923
>> |
>> inum |
>> 2 | -234.5949 168.2358 -1.39 0.169 -571.3555 102.1657
>> 3 | -17.52719 160.0994 -0.11 0.913 -338.0009 302.9465
>> 4 | -55.42242 169.1826 -0.33 0.744 -394.0781 283.2333
>> 5 | -271.0341 187.8332 -1.44 0.154 -647.0231 104.955
>> 6 | -118.9656 166.5016 -0.71 0.478 -452.2547 214.3236
>> 7 | -95.84294 221.8941 -0.43 0.667 -540.0123 348.3264
>> 8 | -206.4776 219.635 -0.94 0.351 -646.1248 233.1696
>> 9 | -234.8429 195.681 -1.20 0.235 -626.5411 156.8552
>> 10 | -83.4776 219.635 -0.38 0.705 -523.1248 356.1696
>> 11 | -451.0993 188.756 -2.39 0.020 -828.9356 -73.26306
>> 12 | -271.1145 218.8122 -1.24 0.220 -709.1148 166.8857
>> 13 | -68.56328 168.2744 -0.41 0.685 -405.4011 268.2746
>> 14 | -55.88331 171.9721 -0.32 0.746 -400.1229 288.3562
>> 15 | -265.9787 191.091 -1.39 0.169 -648.489 116.5315
>> 16 | -125.1974 175.2724 -0.71 0.478 -476.0432 225.6484
>> 17 | -147.8429 221.8941 -0.67 0.508 -592.0123 296.3264
>> 18 | -148.5633 168.2744 -0.88 0.381 -485.4011 188.2746
>> 19 | -181.3566 181.0713 -1.00 0.321 -543.8101 181.097
>> 20 | -297.1145 218.8122 -1.36 0.180 -735.1148 140.8857
>> 21 | -13.84294 221.8941 -0.06 0.950 -458.0123 430.3264
>> 22 | -193.3253 167.3785 -1.16 0.253 -528.3697 141.7191
>> 23 | -160.5306 193.9322 -0.83 0.411 -548.7281 227.6669
>> 24 | -145.7978 165.7959 -0.88 0.383 -477.6744 186.0788
>> 25 | -109.5 181.2121 -0.60 0.548 -472.2354 253.2354
>> 26 | -290.1386 224.4886 -1.29 0.201 -739.5012 159.2241
>> 27 | -183.1603 191.4903 -0.96 0.343 -566.4698 200.1492
>> 28 | -77.4776 219.635 -0.35 0.726 -517.1248 362.1696
>> 29 | -120.0795 177.5111 -0.68 0.501 -475.4067 235.2476
>> 30 | -346.9226 226.8633 -1.53 0.132 -801.0388 107.1937
>> 31 | -204.4776 219.635 -0.93 0.356 -644.1248 235.1696
>> |
>> _cons | 134 147.9591 0.91 0.369 -162.1722 430.1722
>> ------------------------------------------------------------------------------
>>
>> . margins agedum
>>
>> Predictive margins Number of obs = 97
>> Model VCE : OLS
>>
>> Expression : Linear prediction, predict()
>>
>> ------------------------------------------------------------------------------
>> | Delta-method
>> | Margin Std. Err. z P>|z| [95% Conf. Interval]
>> -------------+----------------------------------------------------------------
>> agedum |
>> 1 | -22.27385 49.35852 -0.45 0.652 -119.0148 74.46706
>> 2 | 162.8407 40.73507 4.00 0.000 83.00143 242.68
>> 3 | 83.56909 47.28917 1.77 0.077 -9.115992 176.2542
>> 4 | 52.20375 41.96549 1.24 0.214 -30.0471 134.4546
>> 5 | 148.8647 66.87327 2.23 0.026 17.7955 279.9339
>> 6 | 197.6487 71.66732 2.76 0.006 57.18337 338.1141
>> 7 | 1.493234 70.09108 0.02 0.983 -135.8828 138.8692
>> 8 | 454.8808 75.76749 6.00 0.000 306.3793 603.3824
>> 9 | 573.5592 111.6518 5.14 0.000 354.7258 792.3926
>>
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