Copying output of regression from stata to statalist

[MARIA CHRISTOFIDOU]

I am sorry to come with a very basic question but I have been struggling for hours. I have read the FAQ where it states to use hash icon to paste data but I tried and does not work. Can someone please tell me the exact steps.
I go to stata, highlight the data and go to Edit, copy command.
then I come to this window and I click on the hash icon above and then I do paste. But my numbers are all over the place.
I would be very grateful for some guidance. What am I doing wrong?

[Nick Cox]

Sorry you're having difficulty, but what you describe is what you should be doing and I can't explain why it doesn't work for you.

I ran the code below, copied the results from the Results window in Stata, clicked on # here to get a pair of CODE delimiters and then pasted the results in between. It looks untidy as I write, but it should look tidy when I post. But I don't use Edit -- I just highlight the text wanted and use Ctrl-C in Stata, Ctrl-V here within Windows. Other operating systems, other details.

. sysuse auto, clear
(1978 automobile data)

. regress mpg weight

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(1, 72)        =    134.62
       Model |   1591.9902         1   1591.9902   Prob > F        =    0.0000
    Residual |  851.469256        72  11.8259619   R-squared       =    0.6515
-------------+----------------------------------   Adj R-squared   =    0.6467
       Total |  2443.45946        73  33.4720474   Root MSE        =    3.4389

------------------------------------------------------------------------------
         mpg | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      weight |  -.0060087   .0005179   -11.60   0.000    -.0070411   -.0049763
       _cons |   39.44028   1.614003    24.44   0.000     36.22283    42.65774
------------------------------------------------------------------------------

[MARIA CHRISTOFIDOU]

regress NZS ESGCom PERF REM NZBA PCAF WOMB WOME SIZE LEV ROAA

      Source |       SS           df       MS      Number of obs   =       176
-------------+----------------------------------   F(10, 165)      =     11.10
       Model |    144.5664        10    14.45664   Prob > F        =    0.0000
    Residual |  214.973372       165  1.30286892   R-squared       =    0.4021
-------------+----------------------------------   Adj R-squared   =    0.3659
       Total |  359.539773       175  2.05451299   Root MSE        =    1.1414

------------------------------------------------------------------------------
         NZS | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      ESGCom |  -.0012644   .2686344    -0.00   0.996    -.5316685    .5291396
        PERF |   .4104477   .3865421     1.06   0.290    -.3527586    1.173654
         REM |   1.003892   .4165813     2.41   0.017     .1813745    1.826409
        NZBA |   .8495101   .2844688     2.99   0.003      .287842    1.411178
        PCAF |   .5602583   .2246799     2.49   0.014     .1166401    1.003877
        WOMB |   .6815144   .4893493     1.39   0.166    -.2846793    1.647708
        WOME |  -1.143908   .5399985    -2.12   0.036    -2.210106   -.0777102
        SIZE |   .0770184   .0740764     1.04   0.300    -.0692415    .2232782
         LEV |   .0230312   .0256128     0.90   0.370    -.0275398    .0736022
        ROAA |   .1366201   .0774553     1.76   0.080    -.0163113    .2895514
       _cons |   .2232159   1.256102     0.18   0.859     -2.25689    2.703322
------------------------------------------------------------------------------

Many thanks Nick. It is a great help! My missing link was that I did not know that data would be sorted once I post. What a coincidence you are working with environmental data! My study is on GHG emissions reporting by banks in Europe and their Net Zero Journey. Can I share my results and you tell me whether my model is a good fit,as I am weak in statistics. The above are the results of the second model on the Net Zero Journey. I will post below the results of my first model too. I cannot see the hash symbol now...

[MARIA CHRISTOFIDOU]

a

[MARIA CHRISTOFIDOU]

 reghdfe GERQS ESGCom PERF REM NZBA PCAF WOMB WOME SIZE LEV ROAA, absorb(CCODE) cluster(CCODE)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        176
Absorbing 1 HDFE group                            F(  10,     29) =      10.75
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.6799
                                                  Adj R-squared   =     0.5881
                                                  Within R-sq.    =     0.3529
Number of clusters (CCODE)   =         30         Root MSE        =     5.7533

                                 (Std. err. adjusted for 30 clusters in CCODE)
------------------------------------------------------------------------------
             |               Robust
       GERQS | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      ESGCom |   .5936342   1.051669     0.56   0.577    -1.557271    2.744539
        PERF |   1.664053   2.028009     0.82   0.419    -2.483691    5.811798
         REM |   8.458434   2.455356     3.44   0.002     3.436666     13.4802
        NZBA |    3.76366   2.441842     1.54   0.134    -1.230467    8.757788
        PCAF |   3.509845   1.692764     2.07   0.047     .0477531    6.971937
        WOMB |   .6919396    2.75665     0.25   0.804    -4.946042    6.329921
        WOME |  -3.238755   2.418232    -1.34   0.191    -8.184595    1.707086
        SIZE |   .1634796   .7393921     0.22   0.827    -1.348747    1.675706
         LEV |  -.0783147   .2764249    -0.28   0.779    -.6436671    .4870377
        ROAA |   .6812615    .397198     1.72   0.097    -.1310996    1.493623
       _cons |   3.536742   12.21612     0.29   0.774    -21.44802     28.5215
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
       CCODE |        30          30           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.

[Nick Cox]

MARIA CHRISTOFIDOU In #3 and #5 you give some regression results, but I am not clear what the variables are, or what questions you are asking. I doubt whether that is clear to anyone.

I'd start a new thread if you want advice on your modelling. (The title of the thread is now quite irrelevant.)

Some simple thoughts are that I've never seen pollution data that wasn't highly skewed and for which relationships were nonlinear and which did not work better as a generalized linear model than a plain regression.

I have never used reghdfe (from SSC, as you are asked to explain?) and I suspect you need economists to comment there.

[Nick Cox]

You are in fact running a thread on your modelling concurrently at https://www.statalist.org/forums/for...nal-regression