Dear Gretl community, I was estimating an OLS model using GUI and when I performed some tests on results I realized that heteroskedasticity tests - White's test, White's test (squared only), Breusch-Pagan, and Koenker - give a little bit confusing results (IMHO), look:
White's test for heteroskedasticity - Null hypothesis: heteroskedasticity not present Test statistic: LM = 51.4256 with p-value = P(Chi-Square(35) > 51.4256) = 0.0361713 White's test for heteroskedasticity - Null hypothesis: heteroskedasticity not present Test statistic: LM = 16.9311 with p-value = P(Chi-Square(14) > 16.9311) = 0.259869 Breusch-Pagan test for heteroskedasticity - Null hypothesis: heteroskedasticity not present Test statistic: LM = 7.01643 with p-value = P(Chi-Square(7) > 7.01643) = 0.427172 Breusch-Pagan test for heteroskedasticity - Null hypothesis: heteroskedasticity not present Test statistic: LM = 8.8388 with p-value = P(Chi-Square(7) > 8.8388) = 0.264438 With this results we aren't able to differentiate between the first and second results (and also the third and fourth results) because they are presented with the same title. I think it could be better if we have: White's test for heteroskedasticity - White's test for heteroskedasticity (squared only) - Breusch-Pagan test for heteroskedasticity - Breusch-Pagan test for heteroskedasticity (Koenker robust variant) - What do you think? Best, Henrique C. de Andrade Doutorando em Economia Aplicada Universidade Federal do Rio Grande do Sul www.ufrgs.br/ppgeDear Gretl community,
I was estimating an OLS model using GUI and when I performed some tests on results I realized that heteroskedasticity tests - White's test, White's test (squared only), Breusch-Pagan, and Koenker - give a little bit confusing results (IMHO), look:
White's test for heteroskedasticity -
Null hypothesis: heteroskedasticity not present
Test statistic: LM = 51.4256
with p-value = P(Chi-Square(35) > 51.4256) = 0.0361713
White's test for heteroskedasticity -
Null hypothesis: heteroskedasticity not present
Test statistic: LM = 16.9311
with p-value = P(Chi-Square(14) > 16.9311) = 0.259869
Breusch-Pagan test for heteroskedasticity -
Null hypothesis: heteroskedasticity not present
Test statistic: LM = 7.01643
with p-value = P(Chi-Square(7) > 7.01643) = 0.427172
Breusch-Pagan test for heteroskedasticity -
Null hypothesis: heteroskedasticity not present
Test statistic: LM = 8.8388
with p-value = P(Chi-Square(7) > 8.8388) = 0.264438
With this results we aren't able to differentiate between the first and second results (and also the third and fourth results) because they are presented with the same title. I think it could be better if we have:
White's test for heteroskedasticity -
White's test for heteroskedasticity (squared only) -
Breusch-Pagan test for heteroskedasticity -
Breusch-Pagan test for heteroskedasticity (Koenker robust variant) -
What do you think?
Best,
Henrique C. de Andrade
Doutorando em Economia Aplicada
Universidade Federal do Rio Grande do Sul
www.ufrgs.br/ppge
