Hi,
I am using a fixed effects model, but I am wondering why my results are significant when I use vce(robust) and become insignificant when I use vce(cluster company). The coefficients remain the same only the degrees of freedom are different. Can someone explain to me the difference between vce(robust) and vce(cluster company), and why my coefficients become insignificant? Further can someone explain to me what the degrees of freedom mean in this analysis?
Below are my two outputs:
I am using a fixed effects model, but I am wondering why my results are significant when I use vce(robust) and become insignificant when I use vce(cluster company). The coefficients remain the same only the degrees of freedom are different. Can someone explain to me the difference between vce(robust) and vce(cluster company), and why my coefficients become insignificant? Further can someone explain to me what the degrees of freedom mean in this analysis?
Below are my two outputs:
Code:
. reghdfe bda announcement_eligible announcement l_lnassets l_roa, absorb(t incorp indus c) vce(robust)
(converged in 3 iterations)
note: announcement omitted because of collinearity
HDFE Linear regression Number of obs = 3,944
Absorbing 4 HDFE groups F( 3, 3426) = 3.17
Statistics robust to heteroskedasticity Prob > F = 0.0235
R-squared = 0.8970
Adj R-squared = 0.8815
Within R-sq. = 0.0393
Root MSE = 0.1509
---------------------------------------------------------------------------------------
| Robust
bda | Coef. Std. Err. t P>|t| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
announcement_eligible | .0123368 .0070637 1.75 0.081 -.0015127 .0261863
announcement | 0 (omitted)
l_lnassets | -.0151639 .0218286 -0.69 0.487 -.0579624 .0276345
l_roa | -.8755667 .3432176 -2.55 0.011 -1.548499 -.2026348
---------------------------------------------------------------------------------------
Absorbed degrees of freedom:
------------------------------------------------------------------------+
Absorbed FE | Num. Coefs. = Categories - Redundant |
----------------------+-------------------------------------------------|
t | 8 8 0 |
incorp | 15 16 1 |
indus | 55 56 1 ? |
c | 437 493 56 ? |
------------------------------------------------------------------------+
? = number of redundant parameters may be higher
Code:
reghdfe bda announcement_eligible announcement l_lnassets l_roa, absorb(t incorp indus c) vce(cluster c)
(converged in 3 iterations)
note: announcement omitted because of collinearity
HDFE Linear regression Number of obs = 3,944
Absorbing 4 HDFE groups F( 3, 492) = 1.62
Statistics robust to heteroskedasticity Prob > F = 0.1827
R-squared = 0.8970
Adj R-squared = 0.8795
Within R-sq. = 0.0393
Number of clusters (c) = 493 Root MSE = 0.1521
(Std. Err. adjusted for 493 clusters in c)
---------------------------------------------------------------------------------------
| Robust
bda | Coef. Std. Err. t P>|t| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
announcement_eligible | .0123368 .0149413 0.83 0.409 -.0170198 .0416935
announcement | 0 (omitted)
l_lnassets | -.0151639 .0309545 -0.49 0.624 -.0759833 .0456554
l_roa | -.8755667 .5327938 -1.64 0.101 -1.922399 .1712652
---------------------------------------------------------------------------------------
Absorbed degrees of freedom:
------------------------------------------------------------------------+
Absorbed FE | Num. Coefs. = Categories - Redundant |
----------------------+-------------------------------------------------|
t | 8 8 0 |
incorp | 15 16 1 |
indus | 55 56 1 ? |
c | 0 493 493 * |
------------------------------------------------------------------------+
? = number of redundant parameters may be higher
* = fixed effect nested within cluster; treated as redundant for DoF computation

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