Dear Stata Community
In my paper, I want to investigate the impact of import competition on the incidence of zombie firms. The variables import competition (=penetration) and share of zombie firms (=share_zombiesBH1) are industry-based variables. The variable sic defines in which sector the firm is. Gvkey is the unique identifier for each firm. and at is the number of total assets by each firm.
The datatable looks as follows:
If I now run the regression, I get the following output. As it can be seen in the output, Stata assumes that the variable gvkey is the group variable, whereas sic should be the group variable. How can I solve this problem?
Many thanks for your help.
Roman
In my paper, I want to investigate the impact of import competition on the incidence of zombie firms. The variables import competition (=penetration) and share of zombie firms (=share_zombiesBH1) are industry-based variables. The variable sic defines in which sector the firm is. Gvkey is the unique identifier for each firm. and at is the number of total assets by each firm.
The datatable looks as follows:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long gvkey float sic double year float(penetration share_zombiesBH1) double at 1934 20 1989 .03984093 .030927835 184.433 10271 20 1989 .03984093 .030927835 133.559 8935 20 1989 .03984093 .030927835 4381.7 5824 20 1989 .03984093 .030927835 3717.6 4054 20 1989 .03984093 .030927835 17.268 15131 20 1989 .03984093 .030927835 2.884 1369 20 1989 .03984093 .030927835 139.793 15090 20 1989 .03984093 .030927835 1.486 11748 20 1989 .03984093 .030927835 95.965 1498 20 1989 .03984093 .030927835 423.518 5848 20 1989 .03984093 .030927835 1352.919 13324 20 1989 .03984093 .030927835 21.304 5597 20 1989 .03984093 .030927835 1814.101 6544 20 1989 .03984093 .030927835 2946 8852 20 1989 .03984093 .030927835 3221.9 4809 20 1989 .03984093 .030927835 448.037 13641 20 1989 .03984093 .030927835 7.981 13607 20 1989 .03984093 .030927835 1.987 1722 20 1989 .03984093 .030927835 4728.308 7507 20 1989 .03984093 .030927835 1841.913 12825 20 1989 .03984093 .030927835 66.594 6102 20 1989 .03984093 .030927835 844.339 2663 20 1989 .03984093 .030927835 3932.1 9303 20 1989 .03984093 .030927835 111.086 3138 20 1989 .03984093 .030927835 448.532 2909 20 1989 .03984093 .030927835 40.322 3362 20 1989 .03984093 .030927835 4804.161 5568 20 1989 .03984093 .030927835 4487.451 1729 20 1989 .03984093 .030927835 4.372 7770 20 1989 .03984093 .030927835 380.202 13318 20 1989 .03984093 .030927835 186.896 12409 20 1989 .03984093 .030927835 289.361 10793 20 1989 .03984093 .030927835 2586.08 12785 20 1989 .03984093 .030927835 291.102 10177 20 1989 .03984093 .030927835 9.145 14356 20 1989 .03984093 .030927835 42.618 5141 20 1989 .03984093 .030927835 754.733 13323 20 1989 .03984093 .030927835 81.168 12736 20 1989 .03984093 .030927835 2.685 14891 20 1989 .03984093 .030927835 104.623 13592 20 1989 .03984093 .030927835 115.337 7146 20 1989 .03984093 .030927835 864.511 5185 20 1989 .03984093 .030927835 191.928 13864 20 1989 .03984093 .030927835 6.809 5599 20 1989 .03984093 .030927835 171.749 8479 20 1989 .03984093 .030927835 15126.7 14273 20 1989 .03984093 .030927835 243.038 1462 20 1989 .03984093 .030927835 178.954 12201 20 1989 .03984093 .030927835 161.111 4078 20 1989 .03984093 .030927835 139.408 10899 20 1989 .03984093 .030927835 111.94 14070 20 1989 .03984093 .030927835 329.232 6375 20 1989 .03984093 .030927835 3390.4 15000 20 1989 .03984093 .030927835 51.464 9433 20 1989 .03984093 .030927835 481.846 11791 20 1989 .03984093 .030927835 39.347 20338 20 1989 .03984093 .030927835 34.025 3013 20 1989 .03984093 .030927835 185.989 11424 20 1989 .03984093 .030927835 47.818 2606 20 1989 .03984093 .030927835 47.165 12309 20 1989 .03984093 .030927835 132.147 14455 20 1989 .03984093 .030927835 213.396 12756 20 1989 .03984093 .030927835 4731.946 9774 20 1989 .03984093 .030927835 164.886 13930 20 1989 .03984093 .030927835 15.11 1408 20 1989 .03984093 .030927835 11394.2 6340 20 1989 .03984093 .030927835 13.616 13136 20 1989 .03984093 .030927835 109.704 10345 20 1989 .03984093 .030927835 113.399 14382 20 1989 .03984093 .030927835 3.464 2435 20 1989 .03984093 .030927835 1020.984 8336 20 1989 .03984093 .030927835 14.301 4050 20 1989 .03984093 .030927835 461.52 2710 20 1989 .03984093 .030927835 139.293 14332 20 1989 .03984093 .030927835 749.157 11902 20 1989 .03984093 .030927835 28.139 2674 20 1989 .03984093 .030927835 382.507 3245 20 1989 .03984093 .030927835 25.191 14057 20 1989 .03984093 .030927835 194.622 2562 20 1989 .03984093 .030927835 3704.7 5709 20 1989 .03984093 .030927835 727.429 11713 20 1989 .03984093 .030927835 57.376 19437 20 1989 .03984093 .030927835 1.733 3144 20 1989 .03984093 .030927835 8282.536 9411 20 1989 .03984093 .030927835 6522.732 2675 20 1989 .03984093 .030927835 781.051 13830 20 1989 .03984093 .030927835 8.531 13063 20 1989 .03984093 .030927835 17.732 12614 20 1989 .03984093 .030927835 70.89 15087 20 1989 .03984093 .030927835 1.894 8582 20 1989 .03984093 .030927835 193.591 3657 20 1989 .03984093 .030927835 479.687 1663 20 1989 .03984093 .030927835 9025.7 2597 20 1989 .03984093 .030927835 3434.042 12566 20 1989 .03984093 .030927835 135.253 3821 20 1989 .03984093 .030927835 744.759 10551 20 1989 .03984093 .030927835 116.692 11790 21 1989 .003395724 0 432.161 3642 21 1989 .003395724 0 392.816 1932 21 1989 .003395724 0 18655.548 end
Code:
xtreg share_zombiesBH1 penetration at, fe
Fixed-effects (within) regression Number of obs = 39,091
Group variable: gvkey Number of groups = 4,927
R-sq: Obs per group:
within = 0.1767 min = 1
between = 0.1245 avg = 7.9
overall = 0.1052 max = 23
F(2,34162) = 3667.10
corr(u_i, Xb) = -0.5759 Prob > F = 0.0000
------------------------------------------------------------------------------
share_zomb~1 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
penetration | .4333692 .0052005 83.33 0.000 .4231761 .4435624
at | 1.87e-07 2.61e-08 7.14 0.000 1.35e-07 2.38e-07
_cons | .0014647 .0009884 1.48 0.138 -.0004727 .003402
-------------+----------------------------------------------------------------
sigma_u | .05474494
sigma_e | .03663516
rho | .69069127 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(4926, 34162) = 10.17 Prob > F = 0.0000
Roman
Comment