Hello everyone,
I hope you are well.
I'm wondering about the mixed models.
Here is my order:
Here are the results :
I specify first || CountryofHeadquarters: then || entFE: // so I specify first the country level then the company level, is that correct?
Indeed, if I do the opposite, the No of groups remain similar on both levels (country and company).
Secondly, with regard to vce(robust) robust standard errors, is it really worth adding them, given that robust standard errors don't take intra-group or intra-cluster correlation structure into account?
Is it better to use the vce(cluster) option? If so, which cluster should I specify? entFE? CountryofHeadquarters?
Finally, if I include the vce(robust) option in my analysis, my prob>chi2 stat doesn't appear (the stat does appear if I set only i.Year or only i.NAICS2digit, but if I include both in the model, it doesn't appear).
Thank you very much in advance,
Loïc Dubois
I hope you are well.
I'm wondering about the mixed models.
Here is my order:
Code:
mixed Qtobin_w mills centBWOMEN c.centBWOMEN#i.quota BoardSize indep CEOChairmanDuality SIZE_w lnAGE leverage_w ESGScore uncemployement popgrwoth corruption GDPgrowth Dummylegalsystem quota i.Year i.NAICS2digit || CountryofHeadquarters: || entFE:, mle
Code:
mixed Qtobin_w centBWOMEN c.centBWOMEN#i.quota BoardSize indep CEOChairmanDuality SIZE_w lnAGE leverage_w ESGScore uncemployement popgrwoth corrup
> tion GDPgrowth Dummylegalsystem quota i.Year i.NAICS2digit || CountryofHeadquarters: || entFE:, mle
Performing EM optimization ...
Performing gradient-based optimization:
Iteration 0: log likelihood = -52779.705
Iteration 1: log likelihood = -52779.705
Computing standard errors ...
Mixed-effects ML regression Number of obs = 48,134
Grouping information
-------------------------------------------------------------
| No. of Observations per group
Group variable | groups Minimum Average Maximum
----------------+--------------------------------------------
CountryofH~s | 40 10 1,203.3 17,582
entFE | 7,080 2 6.8 19
-------------------------------------------------------------
Wald chi2(53) = 4631.52
Log likelihood = -52779.705 Prob > chi2 = 0.0000
------------------------------------------------------------------------------------
Qtobin_w | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------------+----------------------------------------------------------------
centBWOMEN | .1165522 .042965 2.71 0.007 .0323423 .2007621
|
quota#c.centBWOMEN |
1 | .2555075 .111079 2.30 0.021 .0377966 .4732183
|
BoardSize | .0097224 .0016394 5.93 0.000 .0065092 .0129356
indep | .0178116 .027422 0.65 0.516 -.0359345 .0715577
CEOChairmanDuality | .0260314 .0101111 2.57 0.010 .006214 .0458488
SIZE_w | -.277437 .0065622 -42.28 0.000 -.2902987 -.2645752
lnAGE | -.0843648 .008927 -9.45 0.000 -.1018613 -.0668682
leverage_w | -.0647433 .0052194 -12.40 0.000 -.0749731 -.0545135
ESGScore | .0028652 .0003365 8.52 0.000 .0022057 .0035246
uncemployement | -2.323016 .2430006 -9.56 0.000 -2.799288 -1.846744
popgrwoth | 4.925222 .9433319 5.22 0.000 3.076325 6.774118
corruption | -.1158873 .025226 -4.59 0.000 -.1653292 -.0664453
GDPgrowth | .0258892 .0020749 12.48 0.000 .0218224 .0299559
Dummylegalsystem | -.091007 .218237 -0.42 0.677 -.5187437 .3367297
quota | -.0210635 .0241421 -0.87 0.383 -.0683812 .0262542
Indeed, if I do the opposite, the No of groups remain similar on both levels (country and company).
Code:
. mixed Qtobin_w centBWOMEN c.centBWOMEN#i.quota BoardSize indep CEOChairmanDuality SIZE_w lnAGE leverage_w ESGScore uncemployement popgrwoth corrup
> tion GDPgrowth Dummylegalsystem quota i.Year i.NAICS2digit || entFE: || CountryofHeadquarters:, mle
Performing EM optimization ...
Performing gradient-based optimization:
Iteration 0: log likelihood = -53063.497 (not concave)
Iteration 1: log likelihood = -53063.497 (backed up)
Computing standard errors ...
Mixed-effects ML regression Number of obs = 48,134
Grouping information
-------------------------------------------------------------
| No. of Observations per group
Group variable | groups Minimum Average Maximum
----------------+--------------------------------------------
entFE | 7,080 2 6.8 19
CountryofH~s | 7,080 2 6.8 19
-------------------------------------------------------------
Wald chi2(53) = 4487.85
Log likelihood = -53063.497 Prob > chi2 = 0.0000
------------------------------------------------------------------------------------
Qtobin_w | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------------+----------------------------------------------------------------
centBWOMEN | .0492723 .0427491 1.15 0.249 -.0345144 .1330589
|
quota#c.centBWOMEN |
1 | .0275504 .1095757 0.25 0.801 -.1872141 .2423148
Secondly, with regard to vce(robust) robust standard errors, is it really worth adding them, given that robust standard errors don't take intra-group or intra-cluster correlation structure into account?
Is it better to use the vce(cluster) option? If so, which cluster should I specify? entFE? CountryofHeadquarters?
Finally, if I include the vce(robust) option in my analysis, my prob>chi2 stat doesn't appear (the stat does appear if I set only i.Year or only i.NAICS2digit, but if I include both in the model, it doesn't appear).
Thank you very much in advance,
Loïc Dubois