I am currently working on my master thesis : i am analyzing the link between board gender diversity and firm performance. My sample consists of 264 firms from the S&P500 over the 2006-2012 period.
I am using Stata/SE 15.1 and for the regression i am using reghdfe
my model is the following one :
firm value= B0 + B1[%WomenOnBoard] + B2[control variables] + Year fixed effects*Industry fixed effects + Firm fixed effects + e
My problem is when I want to see how the firms reacted during the subprime crisis and outside the crisis. Therefore, i added interaction terms and worked with dummy variables (Crisis and PostCrisis)
Crisis = 1 when year = 2008 & 2009, 0 otherwise and PostCrisis when year = 2010, 2011, 2012 and 0 otherwise.
firm value= B0 + B1[%women*Crisis] + B1[%women*Post_Crisis] + B3[control variables] + Year fixed effects * Industry Fixed effects + Firm fixed effects + e
Code:
. reghdfe Q c.WOMENONBOARD_w#i.Crisis c.WOMENONBOARD_w#i.PostCrisis BOARDSIZE_w FIRMSIZELNAT_w CASHHO
> LDINGSCHEAT_w LEVV_w, absorb(i.DataYearFiscal#SIC_group FIRM) vce(cluster FIRM)
(MWFE estimator converged in 2 iterations)
note: 1.PostCrisis#c.WOMENONBOARD_w omitted because of collinearity
HDFE Linear regression Number of obs = 1,848
Absorbing 2 HDFE groups F( 7, 263) = 17.15
Statistics robust to heteroskedasticity Prob > F = 0.0000
R-squared = 0.8905
Adj R-squared = 0.8701
Within R-sq. = 0.2031
Number of clusters (FIRM) = 264 Root MSE = 0.1537
(Std. Err. adjusted for 264 clusters in FIRM)
---------------------------------------------------------------------------------------------
| Robust
Q | Coef. Std. Err. t P>|t| [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
Crisis#c.WOMENONBOARD_w |
0 | -.1269574 .1403645 -0.90 0.367 -.4033387 .1494238
1 | -.1456752 .2077467 -0.70 0.484 -.5547336 .2633831
|
PostCrisis#c.WOMENONBOARD_w |
0 | .1364259 .1710807 0.80 0.426 -.2004364 .4732881
1 | 0 (omitted)
|
BOARDSIZE_w | -.0001824 .0042915 -0.04 0.966 -.0086325 .0082676
FIRMSIZELNAT_w | -.3358434 .0323371 -10.39 0.000 -.3995159 -.2721709
CASHHOLDINGSCHEAT_w | .2864215 .1309255 2.19 0.030 .0286258 .5442171
LEVV_w | .0014819 .0013261 1.12 0.265 -.0011292 .0040929
_cons | 3.758162 .3072915 12.23 0.000 3.153097 4.363227
---------------------------------------------------------------------------------------------
Absorbed degrees of freedom:
--------------------------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
----------------------------+---------------------------------------|
DataYearFiscal#SIC_group | 21 0 21 |
FIRM | 264 264 0 *|
--------------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
Someone recommended me to do a difference in differences, so i wrote the following code :
Code:
. reghdfe Q c.WOMENONBOARD_w##i.Crisis BOARDSIZE_w FIRMSIZELNAT_w CASHHOLDINGSCHEAT_w LEVV_w, absorb
> (i.DataYearFiscal#SIC_group FIRM) vce(cluster FIRM)
note: 1bn.Crisis is probably collinear with the fixed effects (all partialled-out values are close to
> zero; tol = 1.0e-09)
(MWFE estimator converged in 2 iterations)
note: 1.Crisis omitted because of collinearity
HDFE Linear regression Number of obs = 1,848
Absorbing 2 HDFE groups F( 6, 263) = 19.36
Statistics robust to heteroskedasticity Prob > F = 0.0000
R-squared = 0.8904
Adj R-squared = 0.8700
Within R-sq. = 0.2023
Number of clusters (FIRM) = 264 Root MSE = 0.1537
(Std. Err. adjusted for 264 clusters in FIRM)
-----------------------------------------------------------------------------------------
| Robust
Q | Coef. Std. Err. t P>|t| [95% Conf. Interval]
------------------------+----------------------------------------------------------------
WOMENONBOARD_w | -.0734974 .1215175 -0.60 0.546 -.3127683 .1657736
1.Crisis | 0 (omitted)
|
Crisis#c.WOMENONBOARD_w |
1 | .0650714 .0907979 0.72 0.474 -.1137119 .2438547
|
BOARDSIZE_w | .0000317 .0043224 0.01 0.994 -.0084793 .0085427
FIRMSIZELNAT_w | -.3352275 .0325952 -10.28 0.000 -.3994084 -.2710467
CASHHOLDINGSCHEAT_w | .293405 .1303574 2.25 0.025 .0367281 .5500819
LEVV_w | .0014913 .0013221 1.13 0.260 -.001112 .0040946
_cons | 3.748505 .3097566 12.10 0.000 3.138586 4.358423
-----------------------------------------------------------------------------------------
Absorbed degrees of freedom:
--------------------------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
----------------------------+---------------------------------------|
DataYearFiscal#SIC_group | 21 0 21 |
FIRM | 264 264 0 *|
--------------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
Code:
. reghdfe Q i.quart##i.Crisis BOARDSIZE_w FIRMSIZELNAT_w CASHHOLDINGSCHEAT_w LEVV_w, absorb(i.DataYe
> arFiscal#SIC_group FIRM) vce(cluster FIRM)
note: 1bn.Crisis is probably collinear with the fixed effects (all partialled-out values are close to
> zero; tol = 1.0e-09)
(MWFE estimator converged in 2 iterations)
note: 1.Crisis omitted because of collinearity
HDFE Linear regression Number of obs = 1,848
Absorbing 2 HDFE groups F( 10, 263) = 13.52
Statistics robust to heteroskedasticity Prob > F = 0.0000
R-squared = 0.8909
Adj R-squared = 0.8702
Within R-sq. = 0.2054
Number of clusters (FIRM) = 264 Root MSE = 0.1536
(Std. Err. adjusted for 264 clusters in FIRM)
-------------------------------------------------------------------------------------
| Robust
Q | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
quart |
2 | .0034021 .021553 0.16 0.875 -.0390362 .0458405
3 | -.0131702 .0222317 -0.59 0.554 -.056945 .0306046
4 | -.0298918 .0262545 -1.14 0.256 -.0815876 .021804
|
1.Crisis | 0 (omitted)
|
quart#Crisis |
2 1 | .0278492 .0246207 1.13 0.259 -.0206295 .076328
3 1 | .0252918 .0217393 1.16 0.246 -.0175134 .068097
4 1 | .0058297 .0234859 0.25 0.804 -.0404146 .052074
|
BOARDSIZE_w | -.0005533 .0043108 -0.13 0.898 -.0090414 .0079347
FIRMSIZELNAT_w | -.3349989 .0320547 -10.45 0.000 -.3981154 -.2718824
CASHHOLDINGSCHEAT_w | .284658 .1298826 2.19 0.029 .028916 .5404
LEVV_w | .0014533 .0013958 1.04 0.299 -.001295 .0042016
_cons | 3.750724 .3039763 12.34 0.000 3.152187 4.34926
-------------------------------------------------------------------------------------
Absorbed degrees of freedom:
--------------------------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
----------------------------+---------------------------------------|
DataYearFiscal#SIC_group | 21 0 21 |
FIRM | 264 264 0 *|
--------------------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
Is diff-and-diffs a better option, however there is still an omitted coefficient ? and i am not sure about how to interpret the results?
Thanks in advance for your precious help.
Stephan YAN
0 Response to Interaction terms in panel data
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