I am running a regression with Log stock returns on 1st quarter of 2020 as my dependent variable;
Code:
Ln (Stock price on 31-03-2020/ Stock price on 01-01-2020)
Code:
reg quart_ret i.ff48,vce(cluster ff48)
Code:
. reg quart_ret i.ff48,vce(cluster ff48)
Linear regression Number of obs = 1924
F( 0, 29) = .
Prob > F = .
R-squared = 0.0586
Root MSE = .30878
(Std. Err. adjusted for 30 clusters in ff48)
------------------------------------------------------------------------------
| Robust
quart_ret | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ff48 |
5 | -.0006924 1.29e-15 -5.4e+11 0.000 -.0006924 -.0006924
7 | .0184726 1.29e-15 1.4e+13 0.000 .0184726 .0184726
8 | -.0564544 1.29e-15 -4.4e+13 0.000 -.0564544 -.0564544
9 | -.0447615 1.29e-15 -3.5e+13 0.000 -.0447615 -.0447615
10 | .1450836 1.29e-15 1.1e+14 0.000 .1450836 .1450836
11 | .1361723 1.29e-15 1.1e+14 0.000 .1361723 .1361723
13 | .1527319 1.43e-15 1.1e+14 0.000 .1527319 .1527319
14 | .0309398 1.30e-15 2.4e+13 0.000 .0309398 .0309398
16 | .0442209 1.29e-15 3.4e+13 0.000 .0442209 .0442209As the results indicate all my standard errors are very big, bizarre (+ve &-ve) and F statistic is missing. My intention of clustering by industries is to account for correlation among firms in the same industry but I know that during this period correlation can exist amongst industries also. Hence I tried the following command by clustering at the company level and my results are attached
Code:
reg quart_ret i.ff48,vce(cluster companyname)
Linear regression Number of obs = 1924
F( 29, 1923) = 4.70
Prob > F = 0.0000
R-squared = 0.0586
Root MSE = .30878
(Std. Err. adjusted for 1924 clusters in companyname)
------------------------------------------------------------------------------
| Robust
quart_ret | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ff48 |
5 | -.0006924 .0914865 -0.01 0.994 -.1801155 .1787308
7 | .0184726 .0776587 0.24 0.812 -.1338315 .1707766
8 | -.0564544 .0526913 -1.07 0.284 -.1597924 .0468836
9 | -.0447615 .0512153 -0.87 0.382 -.1452049 .0556818
10 | .1450836 .0650334 2.23 0.026 .0175402 .2726271
11 | .1361723 .0814712 1.67 0.095 -.0236089 .2959536
13 | .1527319 .0408991 3.73 0.000 .0725207 .2329432
14 | .0309398 .0304394 1.02 0.310 -.0287578 .0906375
16 | .0442209 .0357533 1.24 0.216 -.0258985 .1143403
17 | -.0232826 .0417664 -0.56 0.577 -.1051948 .0586297
18 | -.1212549 .0403227 -3.01 0.003 -.2003357 -.042174
19 | -.0859658 .036367 -2.36 0.018 -.1572888 -.0146428
21 | -.0131784 .0371308 -0.35 0.723 -.0859993 .059642Thanks in advance
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