Hi, I am running fe panel data regression on a set of companies to explore the effect of firm geographic segment (GS) diversification on firm firm performance (ROA.) My time series includes 2001-2019. I have divided the data into pre-crisis (2001-2006) and post crisis (2010-2019) periods. The regression results show that:
a. the effect of quadratic geographic segment (GS) diversification on firm performance is significant if I include crisis as a moderator - however -
b. if I run the regression for the pre-crisis (2001-2006) period and post crisis (2010-2019) period separately and drop the moderator "Crisis", the effect of geographic segment (GS) diversification on firm performance is no longer significant.

I am wondering how to interpret this result and was hoping I could gets some guidance from this group. I am pasting my results below:

a. Regression effect of quadratic geographic segment (GS) diversification on firm performance is significant with "crisis" as a moderator

Code:
. xtreg ROA_win05 LnRev TDTE Co_Age c.l1.GSFinal##c.l1.GSFinal##Crisis if Excl_bynd_delisting !=
> 1 & Year !=7 & Year !=8 & Year !=9, fe

Fixed-effects (within) regression               Number of obs     =      1,486
Group variable: ID                              Number of groups  =        176

R-sq:                                           Obs per group:
     within  = 0.1685                                         min =          1
     between = 0.1286                                         avg =        8.4
     overall = 0.1064                                         max =         15

                                                F(8,1302)         =      32.99
corr(u_i, Xb)  = -0.6592                        Prob > F          =     0.0000

----------------------------------------------------------------------------------------------
                   ROA_win05 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
                       LnRev |   1.714848   .1508791    11.37   0.000     1.418855    2.010841
                        TDTE |    .000211   .0005493     0.38   0.701    -.0008667    .0012886
                      Co_Age |  -.2314909   .0404682    -5.72   0.000    -.3108809   -.1521009
                             |
                     GSFinal |
                         L1. |  -15.84082   2.337321    -6.78   0.000    -20.42614   -11.25549
                             |
       cL.GSFinal#cL.GSFinal |   11.38014   2.240617     5.08   0.000     6.984526    15.77575
                             |
                    1.Crisis |  -2.421146   .5444138    -4.45   0.000     -3.48917   -1.353121
                             |
           Crisis#cL.GSFinal |
                          1  |   14.38415     2.3525     6.11   0.000     9.769045    18.99926
                             |
Crisis#cL.GSFinal#cL.GSFinal |
                          1  |  -11.83307   2.295733    -5.15   0.000    -16.33681   -7.329325
                             |
                       _cons |   3.553605   .9702083     3.66   0.000     1.650262    5.456947
-----------------------------+----------------------------------------------------------------
                     sigma_u |   5.249395
                     sigma_e |  3.7141179
                         rho |  .66639907   (fraction of variance due to u_i)
----------------------------------------------------------------------------------------------
F test that all u_i=0: F(175, 1302) = 5.82                   Prob > F = 0.0000

b. Regression effect of quadratic geographic segment (GS) diversification for the pre-crisis (2001-2006) period WITHOUT moderator "Crisis" (post crisis period has similar results and hence I am not pasting that.)

Code:
. xtreg ROA_win05 LnRev TDTE Co_Age c.l1.GSFinal##c.l1.GSFinal if Excl_bynd_delisting !=1 & Year
>  <7, fe

Fixed-effects (within) regression               Number of obs     =        303
Group variable: ID                              Number of groups  =         98

R-sq:                                           Obs per group:
     within  = 0.0905                                         min =          1
     between = 0.1059                                         avg =        3.1
     overall = 0.0901                                         max =          5

                                                F(5,200)          =       3.98
corr(u_i, Xb)  = -0.1687                        Prob > F          =     0.0018

---------------------------------------------------------------------------------------
            ROA_win05 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
                LnRev |    1.79619   .4877115     3.68   0.000      .834474    2.757907
                 TDTE |  -.0029504   .0026559    -1.11   0.268    -.0081877    .0022868
               Co_Age |  -.0180046   .1883957    -0.10   0.924    -.3895014    .3534922
                      |
              GSFinal |
                  L1. |  -1.234931    4.66017    -0.26   0.791     -10.4243     7.95444
                      |
cL.GSFinal#cL.GSFinal |   2.979751   3.566609     0.84   0.404    -4.053233    10.01273
                      |
                _cons |  -2.280197   3.572285    -0.64   0.524    -9.324373    4.763978
----------------------+----------------------------------------------------------------
              sigma_u |  6.3345603
              sigma_e |  3.0909695
                  rho |  .80769024   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------
F test that all u_i=0: F(97, 200) = 7.96                     Prob > F = 0.0000