How is the LM test calculated for Gretl for just a standard model.

I just ran a model

Model 5: OLS, using observations 1-2017

Dependent variable: nettfa

coefficient   std. error   t-ratio    p-value

---------------------------------------------------------

const      -20.9850      2.47202      -8.489    3.98e-017 ***

inc          0.770583    0.0614520    12.54     8.73e-035 ***

age25        0.0251267   0.00259339    9.689    9.96e-022 ***

male         2.47793     2.04778       1.210    0.2264

e401k        6.88622     2.12327       3.243    0.0012    ***

Mean dependent var   13.59498   S.D. dependent var   47.59058

Sum squared resid     3982124   S.E. of regression   44.48805

F(4, 2012)           73.74763   P-value(F)           2.18e-58

Log-likelihood      -10514.46   Akaike criterion     21038.91

Schwarz criterion    21066.96   Hannan-Quinn         21049.21

When I save the residuals squared to run the Pagan test I get this

Model 6: OLS, using observations 1-2017

Dependent variable: usq5

 Coefficient Std. Error t-ratio p-value const -4573.55 1848.7 -2.4739 0.01345 ** inc 112.358 45.9568 2.4449 0.01458 ** age25 4.84866 1.93946 2.5000 0.01250 ** male 2331.25 1531.43 1.5223 0.12810 e401k 1164.83 1587.89 0.7336 0.46330

 Mean dependent var 1974.28 S.D. dependent var 33367.5 Sum squared resid 2.23e+12 S.E. of regression 33270.3 R-squared 0.007789 Adjusted R-squared 0.005817 F(4, 2012) 3.94869 P-value(F) 0.003387 Log-likelihood -23861.3 Akaike criterion 47732.7 Schwarz criterion 47760.8 Hannan-Quinn 47743

The LM test from my understanding is n*r^2, which here would be 15.71

Using gretl’s built in test I get the following:

Breusch-Pagan test for heteroskedasticity

OLS, using observations 1-2017

Dependent variable: scaled uhat^2

coefficient   std. error    t-ratio   p-value

--------------------------------------------------------

const      -2.31657      0.936391      -2.474    0.0134  **

inc         0.0569109    0.0232777      2.445    0.0146  **

age25       0.00245591   0.000982363    2.500    0.0125  **

male        1.18081      0.775689       1.522    0.1281

e401k       0.590001     0.804287       0.7336   0.4633

Explained sum of squares = 4485.49

Test statistic: LM = 2242.746588,

with p-value = P(Chi-square(4) > 2242.746588) = 0.000000

How did the LM test become so big for this model?