Hello together,

we're doing an inverted u-shape analysis on the impact of CSR Score on the TOA of different firms moderated by our time invariant moderator Consistency (Kurtosis of CSR Score).
We would like to investigate if the turning point of the u-shape relationships significantly deviates with different levels of consistency. Therefore we found examples who used the nlcom code using the minimum, maximum, mean, mean +1 SD & mean - 1 SD levels of the moderating variables values.

We ran the following:

Code:
 xtreg roa c.csr_score##c.csr_score##c.consistency ln_firmsize ln_adi ln_rdi slack lev_w industry_growth industry
> _concentration i.fyear, fe cluster(cusipnr)
note: consistency omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =      4,417
Group variable: cusipnr                         Number of groups  =        606

R-squared:                                      Obs per group:
     Within  = 0.0754                                         min =          3
     Between = 0.0699                                         avg =        7.3
     Overall = 0.0472                                         max =         16

                                                F(26,605)         =       4.38
corr(u_i, Xb) = -0.3052                         Prob > F          =     0.0000

                                                       (Std. err. adjusted for 606 clusters in cusipnr)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                                  roa | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------------------------+----------------------------------------------------------------
                            csr_score |   .0017239   .0005557     3.10   0.002     .0006325    .0028154
                                      |
              c.csr_score#c.csr_score |   -.000018   5.19e-06    -3.46   0.001    -.0000282   -7.77e-06
                                      |
                          consistency |          0  (omitted)
                                      |
            c.csr_score#c.consistency |  -.0007174   .0002731    -2.63   0.009    -.0012538    -.000181
                                      |
c.csr_score#c.csr_score#c.consistency |   7.78e-06   2.65e-06     2.94   0.003     2.58e-06     .000013
                                      |
                          ln_firmsize |  -.0031383   .0062741    -0.50   0.617    -.0154599    .0091834
                               ln_adi |  -.0052481   .0055574    -0.94   0.345    -.0161622     .005666
                               ln_rdi |  -.0486486    .008962    -5.43   0.000     -.066249   -.0310483
                                slack |   -.116426   .0318931    -3.65   0.000    -.1790607   -.0537912
                                lev_w |   .0001744   .0007524     0.23   0.817    -.0013032     .001652
                      industry_growth |   .0262122   .0120354     2.18   0.030      .002576    .0498483
               industry_concentration |  -.0017947   .0170669    -0.11   0.916    -.0353123    .0317229
                                      |
                                fyear |
                                2004  |   .0264904   .0091348     2.90   0.004     .0085506    .0444301
                                2005  |   .0261167   .0113369     2.30   0.022     .0038522    .0483813
                                2006  |   .0290126   .0124341     2.33   0.020     .0045934    .0534318
                                2007  |   .0214981   .0125808     1.71   0.088    -.0032092    .0462054
                                2011  |   .0261853   .0138109     1.90   0.058     -.000938    .0533085
                                2012  |   .0136148   .0139187     0.98   0.328      -.01372    .0409495
                                2013  |   .0172718   .0141745     1.22   0.224    -.0105654    .0451089
                                2014  |   .0222723   .0142534     1.56   0.119    -.0057199    .0502645
                                2015  |   .0169446    .014851     1.14   0.254    -.0122211    .0461104
                                2016  |   .0195737   .0148799     1.32   0.189    -.0096489    .0487963
                                2017  |    .016874   .0156194     1.08   0.280    -.0138008    .0475488
                                2018  |   .0337998   .0159556     2.12   0.035     .0024647    .0651348
                                2019  |    .026212   .0169868     1.54   0.123    -.0071483    .0595723
                                2020  |   .0236843   .0181686     1.30   0.193     -.011997    .0593655
                                2021  |   .0459652   .0183655     2.50   0.013     .0098973     .082033
                                      |
                                _cons |  -.1923473    .059989    -3.21   0.001    -.3101592   -.0745354
--------------------------------------+----------------------------------------------------------------
                              sigma_u |  .13053874
                              sigma_e |  .08199672
                                  rho |  .71707153   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------------------
followed by the nlcom (-5.955 is the minimum of consistency)

Code:
nlcom (_b[csr_score]*_b[c.csr_score#c.csr_score#c.consistency] - _b[c.csr_score#c.csr_score]*_b[c.csr_score#c.co
> nsistency]) / 2*(_b[c.csr_score#c.csr_score]+_b[c.csr_score#c.csr_score#c.consistency]*(-5.954534))^2
We got the error "Maximum number of iterations exceeded." What can be the reasons for this error? Maybe because consistency got omitted in the regression? Or maybe because of our very low coefficients? How can we solve this problem?

Thank you for every little hint