[Richard Williams]

With nearly 15,000 cases it does not particularly surprise me that most coefficients are statistically significant. How substantively meaningful they are, or how consistent they are with theory, I don’t know. I presume you picked these variables because you expected them to have effects, and if so it looks like you are right.

given that you originally said you were analyzing 78 firms, I wonder if you shouldn’t be using something like xtreg instead. Failure to take into account that cases are not independent of each other could distort your standard errors.

[Carlo Lazzaro]

Barbara:
I do share Richard's advice to consider -xtreg- for your panel data regression (admittedly, in my previous replies I focused my attention on the -m:m- issue only).
Rarely a pooled OLS (with clustered standard errors, though, since, as Richard pointed out, your observations are not independent) outperforms -xtreg- when it comes to panel data regression.

[Barbara Heerkens]

Thank you!!
I am trying to do this now by setting -xtset company_id date- and then -xtreg cumulative_abnormal_return connected size leverage i.sic2-
However, all variables get omitted. Do you know how I fix this?

xtreg car conn mv size leverage

Random-effects GLS regression        Number of obs     =    18,673
Group variable: _j        Number of groups  =    71

R-sq:        Obs per group:
within  = 0.0000        min =    263
between = 0.0000        avg =    263.0
overall = 0.0000        max =    263

        Wald chi2(0)      =    .
corr(u_i, X)   = 0 (assumed)        Prob > chi2       =    .

            
car       Coef.   Std. Err.        z    P>z     [95% Conf.    Interval]
            
conn           0  (omitted)
mv           0  (omitted)
size           0  (omitted)
leverage           0  (omitted)
_cons           0  (omitted)
            
sigma_u   .09372371
sigma_e           0
rho           1   (fraction    of    variance due to u_i)

Thank you so much

[Marcos Almeida]

In #11, the coefficients seem to be too low. Therefore, being significantly different from zero is not much under a large sample.

Maybe you could check the effect size by getting the Eta-squared. You may type - estat esize - for that matter.

[Barbara Heerkens]

Thank you Marcos!

I did it, but now struggling to find out on the internet how to interpret the results...
Can you tell me something about that?

reg car conn size leverage i.sic2

      Source |       SS           df       MS      Number of obs   =    14,768
-------------+----------------------------------   F(18, 14749)    =    336.98
       Model |  12.3409787        18  .685609925   Prob > F        =    0.0000
    Residual |  30.0077419    14,749  .002034561   R-squared       =    0.2914
-------------+----------------------------------   Adj R-squared   =    0.2905
       Total |  42.3487206    14,767  .002867794   Root MSE        =    .04511

------------------------------------------------------------------------------
         car |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        conn |  -.0141297   .0009227   -15.31   0.000    -.0159384   -.0123211
        size |  -.0043294   .0003849   -11.25   0.000    -.0050838    -.003575
    leverage |  -.0198001    .002426    -8.16   0.000    -.0245553    -.015045
             |
        sic2 |
         15  |   .0135987   .0023398     5.81   0.000     .0090123     .018185
         20  |   .0535925   .0026292    20.38   0.000      .048439     .058746
         27  |   -.009059   .0024154    -3.75   0.000    -.0137935   -.0043245
         32  |  -.0246615   .0020912   -11.79   0.000    -.0287605   -.0205624
         35  |   .0171415   .0025581     6.70   0.000     .0121274    .0221556
         36  |   -.019383   .0025865    -7.49   0.000    -.0244528   -.0143132
         42  |  -.0813206   .0028621   -28.41   0.000    -.0869308   -.0757105
         50  |    .012346   .0026598     4.64   0.000     .0071324    .0175595
         60  |   .0390304   .0031524    12.38   0.000     .0328513    .0452095
         62  |   .0078944   .0025761     3.06   0.002     .0028449    .0129439
         65  |  -.0444362    .002001   -22.21   0.000    -.0483584    -.040514
         70  |   -.000822   .0021413    -0.38   0.701    -.0050191    .0033751
         73  |  -.0207549   .0025759    -8.06   0.000     -.025804   -.0157059
         75  |  -.0457323    .002867   -15.95   0.000     -.051352   -.0401126
         87  |  -.0552378   .0028689   -19.25   0.000    -.0608612   -.0496145
             |
       _cons |   .0303937   .0050335     6.04   0.000     .0205275    .0402599
------------------------------------------------------------------------------

. estat esize

Effect sizes for linear models

-------------------------------------------------------------------
             Source |   Eta-Squared     df     [95% Conf. Interval]
--------------------+----------------------------------------------
              Model |   .2914133        18     .2791215    .3019523
                    |
               conn |   .0156499         1     .0119291    .0198422
               size |   .0085064         1     .0058099    .0116967
           leverage |   .0044962         1     .0025987    .0069001
               sic2 |   .2610804        15     .2489758    .2717046
-------------------------------------------------------------------

Thank you!!

[Marcos Almeida]

You may easily check the interpretation of Eta-squared, either in a textbook or in the Internet.

That said, and considering I’m seing the output from a smartphone, it seems that only sic2 conveys a large effect size. What is more, it has practically the effect size for the whole model. Hopefully that helps.

[Carlo Lazzaro]

Barbara:
one step behind before interpreting your regression results.
If you have repeated measures on the same units (ie, a panel dataset) and you decide to go pooled OLS (even though you previously considered -xtreg-), you should consider that:
rarely a pooled OLS (with clustered standard errors, though, since, as Richard pointed out in one of his previous replies, your observations are not independent) outperforms -xtreg- when it comes to panel data regression.
That said, assuming that pooled OLS is the way to go (and this should be tested via -xttest0-), you should cluster your standard errors on -panelid- to avoid biased results (however other biases might be present: see -ovtest-).

[Barbara Heerkens]

Thank you Carlo.
After hours of research (yes I am a beginner ) I found that -xtreg- was the way to go yes..
The problem I have is that the 'connected'-dummy is omitted (as well as size and leverage since they are values measured at the end of year t-1, so dont change) when using -xtreg car connected size leverage, fe- , so I only have outcomes for post and post#connected

[Carlo Lazzaro]

Barbara:
one of the many helpful comments made by Nick Cox on daily basis sounds like "We are all beginners; some of us are only more experienced" (he surely wrote the sentence in an English that is slightly better than mine! LOL!). Hence, take your time to get the building blocks of this tricky (but relevant,as it one of the Statalist hits) area of statistical inference. It's plenty of panel data econometrics textbooks; Stata users can find the following one really helpful: https://www.stata.com/bookstore/micr...metrics-stata/.
That said:
- you seemingly used -xtreg, re-;
- your -xtreg. re- output looked terrible, as everything seemed omitted (but there should be a reason about that);
- I would post an example/excerpt of your data via -dataex-, so that interested listers can investigate where the culprit (if any) hides.

[Barbara Heerkens]

Thank you for your kind answer Carlo. I really appreciate it!
I realized the outcomes for the CARs was actually one value for each firm, but just the same value given to each day in the sample. Thereby, actually I only have 78 firms with 78 CARs, making it possible to do two possible things:
1. I could keep one CAR (of the many same) per firm and do an OLS on the CARs.
2. I could do an OLS over all the CARs and use -estat esize- as Marcos mentioned and see that the Connection only explains 1.5% of the variance, as mentioned in #20.

What would be the best option?

Besides looking only at the CARs I also (in need for an answer abou the value of the connection, just trying things myself) created a file that contains the returns of all firms during a period of 180 days before a political event and 30 (or 100) days after the event. I wanted to do a dif in dif with an interaction term for post*connected. Now I have a panel data set again (multiple days, multiple firms, returns for each firm on each date in de data). Struggling with the question on whether to use pooled OLS or Fixed effects model of Random effects model (all the coefficients are the same, just different standard deviations). My stock returns are daily LOG(Pt/Pt-1), does this make them a differenced dependent variable and thereby not appropriate to use a Random Effects model?

Thank you!!

[Barbara Heerkens]

dataex unfortunately doesnt work since it exceeds linesize limit! sorry!

[Carlo Lazzaro]

Barbara:
can't you post an excerpt of your dataset that does not exceed the linesize limit of -dataex-?.

[Barbara Heerkens]

Thank you so much so far Carlo! I think I found the answer to my CAR problem, it was including -cluster(_j)- at the end!

Unfortunately, still stuck with the second question from #25.
I am doubting whether to use reg, xtreg fe or xtreg re. Hausman says Random effects instead of fixed effects and if I assume returns are differenced dependent variable, does this mean I should use Pooled OLS?

Here is a part of my data set, containing mnem (firm specific), size of firm , leverage of firm, connectiondummy, date, _j (firm id), returns on the day, sic2(industry indication) and post-dummy (after event). It all covers 180 days before event and 30 after.

* Example generated by -dataex-. To install: ssc install dataex
clear
input str6 mnem float size double leverage float conn int date byte _j double returns float(sic2 post)
"L:AMMB" 18.083858 .31076370706846657 1 17231 1   .008326290587411585 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17232 1 -.0026639615912759478 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17233 1   .024205893947394176 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17234 1  .0077778295009805895 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17237 1  .0024753614804951195 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17238 1 -.0024753614804951633 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17239 1   -.01841524364696065 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17240 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17241 1   .005283947506173883 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17244 1   .015606657621281916 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17245 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17246 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17247 1   .012674489398972906 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17248 1   .021777717116657845 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17251 1   .004655118032198649 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17252 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17253 1  -.009482002750393723 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17254 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17255 1   .014087752183102433 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17258 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17259 1   .006876133020966459 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17260 1   .004600620161181776 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17261 1  -.009133642659529127 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17262 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17265 1 -.0023431105226191236 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17266 1   .004673647276530804 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17267 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17268 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17269 1   .002202485744435702 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17272 1  -.004533022498347366 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17273 1   .009133642659529165 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17274 1   .004439164529744673 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17275 1  -.011242270435362196 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17276 1   .013501284537338214 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17279 1    .00659643893500656 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17280 1   .008569711293351144 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17281 1   -.00207196755997064 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17282 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17283 1  .0064019583821060605 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17286 1  -.010796577027722198 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17287 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17288 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17289 1   .008636977938633782 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17290 1  -.004242359293017602 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17293 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17294 1 -.0021917507123922287 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17295 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17296 1   .012774288036428825 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17297 1  .0041407194966697255 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17300 1  -.006279578692480972 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17301 1 -.0020417197461193525 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17302 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17303 1   .002041719746119371 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17304 1  -.014941421861605856 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17307 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17308 1   .014941421861605882 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17309 1  .0021388591958112733 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17310 1  -.010582537324036656 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17311 1   -.01095894867227084 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17314 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17315 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17316 1 -.0021352339961162336 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17317 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17318 1   .008699564022770905 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17321 1   .008636977938633782 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17322 1  .0021595990890883954 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17323 1   .002041719746119371 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17324 1 -.0020417197461193525 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17325 1  -.002159599089088408 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17328 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17329 1  .0042013188352077666 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17330 1 -.0020417197461193525 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17331 1   .022663984635943785 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17332 1   .019687257232819853 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17335 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17336 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17337 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17338 1   .018739803112797396 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17339 1   -.00926883160440387 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17342 1  -.021155841615901817 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17343 1  -.016155000072426956 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17344 1    .00815261294711529 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17345 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17346 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17349 1    .01189922329970769 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17350 1     .0135146504701694 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17351 1  -.011529754501785693 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17352 1   .020877986991120482 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17353 1   .003664954086405364 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17356 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17357 1  -.005556483565651285 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17358 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17359 1   .003774856289711801 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17360 1   .009112057490997267 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17363 1  .0018363430399972796 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17364 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17365 1 -.0036804837383675997 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17366 1  -.009151243603092852 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17367 1   .010995384301463145 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17370 1  .0018363430399972796 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17371 1   .008887522351775834 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17372 1  -.003515114093439454 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17373 1  -.001813719411627514 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17374 1  -.016390416188169322 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17377 1   -.00189152947924592 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17378 1   .003774856289711801 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17379 1  -.020884028395023742 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17380 1  -.013721501995634487 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17381 1  -.006045485004409956 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17384 1  -.020818001399641867 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17385 1    .01258912730802056 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17386 1   .006160308704818433 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17387 1   .006074147712193166 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17388 1  -.010136411361319925 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17391 1  .0019837984142046295 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17392 1    .00815261294711529 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17393 1  -.014342686197343423 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17394 1   -.01265128926073575 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17395 1  -.015166150228357718 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17398 1    .02153786079661247 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17399 1 -.0020417197461193525 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17400 1   .006308956060904885 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17401 1    .02234539226422633 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17402 1   .002012023436124152 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17405 1 -.0020120234361241654 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17406 1   .003914850221905606 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17407 1 -.0059362384738730995 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17408 1 -.0019290727582004206 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17409 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17412 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17413 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17414 1    .00786531123207346 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17415 1 -.0019028267857814868 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17416 1  .0019028267857815007 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17419 1  -.007865311232073464 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17420 1  -.012176314040339522 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17421 1  -.004206274836023487 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17422 1 -.0020123423776954473 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17423 1 -.0063089560609049114 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17426 1   .004180578941930639 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17427 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17428 1   .012409257981820025 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17429 1  -.006160308704818496 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17430 1  -.002108229780331789 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17433 1 -.0020123423776954473 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17434 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17435 1   .004120572158027229 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17436 1    .00822887409162133 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17437 1  .0060454850044099895 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17440 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17441 1   .003950461010167907 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17442 1   .003914850221905606 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17443 1  -.009905213911093074 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17444 1  -.004005582325390355 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17447 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17448 1  -.006130829035929553 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17449 1  .0061308290359295345 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17450 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17451 1  .0019560528873663685 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17454 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17455 1  -.004024618274169164 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17456 1   .006074147712193166 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17457 1  -.006074147712193201 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17458 1   .004024618274169192 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17461 1  -.004024618274169164 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17462 1   .004024618274169192 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17463 1 -.0061030835090912965 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17464 1  .0020784652349221666 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17465 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17468 1  -.006160308704818496 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17469 1    .00822887409162133 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17470 1  -.006130829035929553 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17471 1  -.008346994332693257 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17472 1 -.0021388591958112282 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17475 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17476 1 -.0020417197461193525 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17477 1   .014511371688828432 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17478 1                     0 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17479 1  -.008202415627923568 60 0
"L:AMMB" 18.083858 .31076370706846657 1 17482 1 -.0063089560609049114 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17483 1   .004180578941930639 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17484 1                     0 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17485 1   -.01708028105741708 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17486 1                     0 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17489 1   .002103125087764352 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17490 1                     0 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17491 1                     0 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17492 1   -.02220084856010914 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17493 1   .006803105905617493 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17496 1  .0021683385356442075 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17497 1   .006665074092192777 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17498 1   .002236330800920821 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17499 1   .010794585431320636 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17500 1 -.0042637182723628975 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17503 1  .0021917507123922803 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17504 1 -.0043946186456161355 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17505 1  .0073360449182156675 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17506 1   .010723865391773066 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17507 1                     0 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17510 1  -.010723865391773064 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17511 1  -.004364805402450088 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17512 1  -.011107881253757373 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17513 1   -.00225608030422416 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17514 1  -.002267861455822087 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17517 1  .0022678614558220493 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17518 1  -.016129500300535775 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17519 1  -.011899223299707757 60 1
"L:AMMB" 18.083858 .31076370706846657 1 17520 1                     0 60 1
"L:HOLB"  17.91688 .17166835071224346 1 17231 2  .0037929890171391014 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17232 2   .007568707780502156 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17233 2   .021868775068905867 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17234 2  -.003543394287117153 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17237 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17238 2  -.007253358294232217 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17239 2  -.022433719285197738 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17240 2  .0037929890171391014 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17241 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17244 2  .0037601488733067724 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17245 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17246 2   .007504020747187269 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17247 2   .007376560647564524 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17248 2   .010796752581349415 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17251 2   .003590809558015567 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17252 2  -.007134203845132764 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17253 2  -.007253358294232217 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17254 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17255 2  .0036028242992613172 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17258 2 -.0036028242992612843 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17259 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17260 2   .007253358294232208 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17261 2  .0035433942871171205 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17262 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17265 2  -.003543394287117153 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17266 2  .0035433942871171205 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17267 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17268 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17269 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17272 2   .007076702669713399 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17273 2   .010366583540841969 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17274 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17275 2 -.0034033472578652614 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17276 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17279 2  .0034033472578652237 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17280 2 -.0069084475322119185 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17281 2  .0035051002743466376 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17282 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17283 2  .0034033472578652237 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17286 2  .0034500038860443764 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17287 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17288 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17289 2   .003350267522978985 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17290 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17293 2   -.00335026752297899 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17294 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17295 2  -.003450003886044388 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17296 2 -.0034033472578652614 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17297 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17300 2  .0034033472578652237 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17301 2  -.010366583540841962 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17302 2   .006963236282976703 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17303 2  .0034033472578652237 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17304 2  .0034500038860443764 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17307 2  -.003450003886044388 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17308 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17309 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17310 2 -.0069084475322119185 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17311 2   .006908447532211928 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17314 2 -.0069084475322119185 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17315 2  .0035051002743466376 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17316 2  -.003505100274346611 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17317 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17318 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17321 2  .0035051002743466376 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17322 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17323 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17324 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17325 2  -.003505100274346611 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17328 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17329 2 -.0034581360086300725 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17330 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17331 2 -.0034858931116978353 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17332 2  .0034858931116978683 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17335 2  .0034581360086300946 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17336 2 -.0034581360086300725 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17337 2   .013816587426886346 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17338 2  -.003450003886044388 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17339 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17342 2 -.0034033472578652614 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17343 2  -.003505100274346611 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17344 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17345 2  .0035051002743466376 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17346 2  .0034033472578652237 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17349 2 -.0034033472578652614 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17350 2  .0034033472578652237 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17351 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17352 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17353 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17356 2 -.0034033472578652614 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17357 2  -.003505100274346611 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17358 2 -.0034581360086300725 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17359 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17360 2   .006963236282976703 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17363 2  .0034033472578652237 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17364 2 -.0034033472578652614 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17365 2  -.003505100274346611 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17366 2   .006908447532211928 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17367 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17370 2 -.0034033472578652614 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17371 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17372 2  -.003505100274346611 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17373 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17374 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17377 2 -.0034581360086300725 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17378 2  .0034581360086300946 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17379 2  -.006944029120327933 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17380 2  -.018020524980656958 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17381 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17384 2   -.01880075644390575 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17385 2  .0037929890171391014 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17386 2  .0037601488733067724 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17387 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17388 2  -.011463104085633079 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17391 2   .003909966195187154 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17392 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17393 2  -.003909966195187161 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17394 2   -.01177389181447357 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17395 2 -.0039677626862735915 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17398 2    .01574165450074716 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17399 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17400 2   .007702955212326236 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17401 2  .0037601488733067724 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17402 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17405 2 -.0037601488733068258 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17406 2   .015007767426766619 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17407 2  -.003743597806272556 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17408 2   .007376560647564524 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17409 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17412 2 -.0073765606475645505 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17413 2 -.0036954618399919485 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17414 2   .007439059646264503 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17415 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17416 2  -.011247618553459831 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17419 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17420 2 -.0037601488733068258 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17421 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17422 2   .007568707780502156 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17423 2   .003695461839991927 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17426 2 -.0036954618399919485 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17427 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17428 2   .007439059646264503 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17429 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17430 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17433 2  -.003743597806272556 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17434 2   .007376560647564524 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17435 2  -.003632962841291988 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17436 2  .0036329628412920087 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17437 2  -.003632962841291988 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17440 2  .0036329628412920087 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17441 2  -.003632962841291988 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17442 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17443 2  .0036329628412920087 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17444 2  .0036028242992613172 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17447 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17448 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17449 2  -.007235787140553287 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17450 2  -.003743597806272556 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17451 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17454 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17455 2   .003743597806272579 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17456 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17457 2  .0036329628412920087 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17458 2  -.003632962841291988 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17461 2  -.003743597806272556 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17462 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17463 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17464 2   .003743597806272579 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17465 2   .024964554100984873 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17468 2                     0 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17469 2 -.0034581360086300725 60 0
"L:HOLB"  17.91688 .17166835071224346 1 17470 2     .0238622858301339 60 0
end
format %td date

I can now run

xtreg ret post##conn size leverage i.sic2 i.date, re cluster(_j)

or

reg ret post##conn size leverage i.sic2 i.date i._j, cluster(_j)

Thank you already!!

[Carlo Lazzaro]

Barbara:
have you tested your dataset with -xttest0- after panel data regression?
What is the -xttest0- outcome?

[Barbara Heerkens]

Yes, it says to use RE

. xttest0

Breusch and Pagan Lagrangian multiplier test for random effects

        returns[_j,t] = Xb + u[_j] + e[_j,t]

        Estimated results:
                         |       Var     sd = sqrt(Var)
                ---------+-----------------------------
                 returns |    .000223       .0149348
                       e |   .0001829       .0135223
                       u |          0              0

        Test:   Var(u) = 0
                             chibar2(01) =     0.00
                          Prob > chibar2 =   1.0000

But the 1.0000 worries me...