Panel data regression: Robust standard errors

Question

my problem is this: I get NA where I should get some values in the computation of robust standard errors.

I am trying to do a fixed effect panel regression

Answer 1

After some time playing around, it works for me and gives me:

                         Estimate  Std. Error t value  Pr(>|t|)    
(Intercept)            4.5099e-16  5.2381e-16  0.8610  0.389254    
C.MCAP_SEC            -5.9769e-07  1.2677e-07 -4.7149 2.425e-06 ***
C.Impact_change       -5.3908e-04  7.5601e-05 -7.1306 1.014e-12 ***
C.Mom                  3.7560e-04  3.3378e-03  0.1125  0.910406    
C.BM                  -1.6438e-04  1.7368e-05 -9.4645 < 2.2e-16 ***
C.PD                   6.2153e-02  3.8766e-02  1.6033  0.108885    
C.CashGen             -2.7876e-04  1.4031e-02 -0.0199  0.984149    
C.NITA                -8.1792e-02  3.2153e-02 -2.5438  0.010969 *  
C.PE                  -6.6170e-06  4.0138e-06 -1.6485  0.099248 .  
C.PEdummy              1.3143e-02  4.8864e-03  2.6897  0.007154 ** 
factor(DS_CODE)130324 -5.2497e-16  5.2683e-16 -0.9965  0.319028    
factor(DS_CODE)130409 -4.0276e-16  5.2384e-16 -0.7689  0.441986    
factor(DS_CODE)130775 -4.4113e-16  5.2424e-16 -0.8415  0.400089  
...

This leaves us with the question why it doesn't for you. I guess it has something to do with the format of your data. Is everything numeric? I converted the column classes and it looks like that for me:

str(dat)
'data.frame':   48251 obs. of  12 variables:
 $ DS_CODE      : chr  "902172" "902172" "902172" "902172" ...
 $ DNEW         : num  2e+05 2e+05 2e+05 2e+05 2e+05 ...
 $ MCAP_SEC     : num  78122 71421 81907 80010 82462 ...
 $ NITA         : num  0.135 0.135 0.135 0.135 0.135 ...
 $ CashGen      : num  0.198 0.198 0.198 0.198 0.198 ...
 $ BM           : num  0.1074 0.1108 0.097 0.0968 0.0899 ...
 $ PE           : num  57 55.3 63.1 63.2 68 ...
 $ PEdummy      : num  0 0 0 0 0 0 0 0 0 0 ...
 $ L1.retE1M    : num  -0.72492 0.13177 0.00122 0.07214 -0.07332 ...
 $ Mom          : num  0 0 0 0 0 ...
 $ PD           : num  5.41e-54 1.51e-66 3.16e-80 2.87e-79 4.39e-89 ...
 $ Impact_change: num  0 -10.59 -10.43 0.7 -6.97 ...

What does str(data) return for you?