[Prathvajeeth Rajmohan]

No change in statistical significance from OLS to OLS with industry FE and FE regression with firm FE is meaningful. It is no cause for concern. Don't even give it another thought. When you change the predictors in a model, anything can happen!

Hi Clyde sorry hope you dont mind, but I have query that quite relevant to this thread and as your were really helpful I thought Id ask;


Hi have an initial regression and also estimated a median regression of the same initial regression.


I have the initial regression:

Code:
. regress lntobinsq lnassets IR leverage roa cratio rnd div year2016, robust
in this leverage is insignificant.

when I estimate the median regression:

Code:
. qreg lntobinsq lnassets IR leverage roa cratio rnd div year2016
leverage becomes significant.


-Generally I was wondering if its normal for coefficients to change from insignificant to significant (and vice versa) from the OLS regression to the median regression.

-also should the magnitude of the coefficients of the median regression change quite significantly from the initial one?

Thanks so much.

[Carlo Lazzaro]

Prathvajeeth:
I do not see any reason to wonder, there:
- you used two (very) different regression models;
- in OLS you imposed a -robust- standard error.

[Prathvajeeth Rajmohan]

No change in statistical significance from OLS to OLS with industry FE and FE regression with firm FE is meaningful. It is no cause for concern. Don't even give it another thought. When you change the predictors in a model, anything can happen!

Hi Clyde sorry to bring up this thread again but was wondering if I could perhaps ask about something I just noticed.

as you know I am investigating the effect of FX derivative usage on lntobinsq (with control variables added)

have 3 regressions: 2 ols models (1 with industriy dummies) and one fixed effects regression:


ols:

regress lntobinsq lnassets Derivatives10 bookleverage_w1 roa_w1  rnd_rev_w1 cash_to_totalassets_w1 div_yield_w1 year2016 if inlist(year,2015,2016), robust

Linear regression                               Number of obs     =        586
                                                F(8, 577)         =      64.62
                                                Prob > F          =     0.0000
                                                R-squared         =     0.6569
                                                Root MSE          =      .3282

----------------------------------------------------------------------------------------
                       |               Robust
             lntobinsq |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
              lnassets |  -.0280999   .0091509    -3.07   0.002     -.046073   -.0101268
       Derivatives10 |   .0522034   .0310484     1.68   0.093    -.0087782     .113185
       bookleverage_w1 |   .1777102   .0570362     3.12   0.002     .0656864    .2897341
                roa_w1 |   .0831313   .0058784    14.14   0.000     .0715857     .094677
            rnd_rev_w1 |   .0157784   .0034542     4.57   0.000     .0089941    .0225627
cash_to_totalassets_w1 |   .2948839   .1538507     1.92   0.056    -.0072918    .5970596
          div_yield_w1 |  -.0586209   .0094744    -6.19   0.000    -.0772295   -.0400124
              year2016 |  -.0057626   .0266702    -0.22   0.829     -.058145    .0466198
                 _cons |    .267082   .0835158     3.20   0.001       .10305    .4311141
----------------------------------------------------------------------------------------

ols #2 (ind2*) where we have added industry dummies (ind2*) included but not shown here:

.  regress lntobinsq lnassets FXDerivatives10 bookleverage_w1 roa_w1  rnd_rev_w1 cash_to_totalassets_w1 div_yield_w1 year2016 ind2* if inlist(year,2015,2016), robust
note: ind240 omitted because of collinearity
note: ind247 omitted because of collinearity
note: ind249 omitted because of collinearity

Linear regression                               Number of obs     =        586
                                                F(55, 529)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.7518
                                                Root MSE          =     .29155

----------------------------------------------------------------------------------------
                       |               Robust
             lntobinsq |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
              lnassets |  -.0150159   .0102229    -1.47   0.142    -.0350984    .0050665
       Derivatives10 |   .0020754   .0304516     0.07   0.946    -.0577454    .0618962
       bookleverage_w1 |   .0411962   .0605595     0.68   0.497    -.0777704    .1601628
                roa_w1 |   .0743793   .0069553    10.69   0.000     .0607158    .0880428
            rnd_rev_w1 |   .0079455   .0030852     2.58   0.010     .0018847    .0140062
cash_to_totalassets_w1 |   .2033755    .179466     1.13   0.258    -.1491781    .5559291
          div_yield_w1 |   -.051623    .008881    -5.81   0.000    -.0690694   -.0341765
              year2016 |  -.0131488   .0236852    -0.56   0.579    -.0596774    .0333798
                   _cons |   .5296384   .0540485     9.80   0.000     .4234624    .6358144

Fixed effects model (with no industry dummies)

.  xtreg lntobinsq lnassets FXDerivatives10 bookleverage_w1 roa_w1  rnd_rev_w1 cash_to_totalassets_w1 div_yield_w1 year2016 if inlist(year,2015,2016), fe robust

Fixed-effects (within) regression               Number of obs     =        586
Group variable: firmid                          Number of groups  =        306

R-sq:                                           Obs per group:
     within  = 0.3443                                         min =          1
     between = 0.1362                                         avg =        1.9
     overall = 0.1489                                         max =          2

                                                F(8,305)          =      12.98
corr(u_i, Xb)  = -0.7578                        Prob > F          =     0.0000

                                         (Std. Err. adjusted for 306 clusters in firmid)
----------------------------------------------------------------------------------------
                       |               Robust
             lntobinsq |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
              lnassets |  -.4679803   .0687184    -6.81   0.000    -.6032024   -.3327582
       Derivatives10 |   .0544599   .0836445     0.65   0.515    -.1101334    .2190531
       bookleverage_w1 |   .2358255   .1360899     1.73   0.084    -.0319684    .5036195
                roa_w1 |   .0136564   .0069675     1.96   0.051    -.0000541    .0273669
            rnd_rev_w1 |  -.0147865   .0134586    -1.10   0.273      -.04127     .011697
cash_to_totalassets_w1 |  -.1290604   .3411373    -0.38   0.705    -.8003409    .5422201
          div_yield_w1 |  -.0390374   .0083692    -4.66   0.000    -.0555062   -.0225687
              year2016 |   .0248787   .0141368     1.76   0.079    -.0029393    .0526967
                 _cons |    3.83923   .4928974     7.79   0.000      2.86932     4.80914
-----------------------+----------------------------------------------------------------
               sigma_u |   .7890989
               sigma_e |  .12121952
                   rho |  .97694566   (fraction of variance due to u_i)
----------------------------------------------------------------------------------------

My question is: is there any cause for concern/our of the ordinary that the constant term jumps from 0.2-0.5 in the ols regression to 3.8 in the Fixed effects model?

- or is this completely normal/should'nt be stressed about?

Thanks so much, eagerly waiting for your reply.

[Clyde Schechter]

Duplicate post. This same question was raised in a new thread and was answered there.