[Gretl-users] LM Test

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 R-squared 0.127868 Adjusted R-squared 0.126134 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 1974.280 S.D. dependent var 33367.52 Sum squared resid 2.23e+12 S.E. of regression 33270.33 R-squared 0.007789 Adjusted R-squared 0.005817 F(4, 2012) 3.948695 P-value(F) 0.003387 Log-likelihood -23861.35 Akaike criterion 47732.70 Schwarz criterion 47760.75 Hannan-Quinn 47742.99 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?