Heteroskedasticity Test White

[Suresh]

The Heteroskedasticity Test White with and without including cross terms give contradictory results. By including the cross terms the following results lead to rejection of null hypothesis.
Heteroskedasticity Test: White

F-statistic 2.894027 Prob. F(5,3729) 0.0130
Obs*R-squared 14.43739 Prob. Chi-Square(5) 0.0131
Scaled explained SS 1508.234 Prob. Chi-Square(5) 0.0000

On the other hand if I am not including cross terms, the results accepts the null hypotheis
Heteroskedasticity Test: White

F-statistic 2.224166 Prob. F(2,3732) 0.1083
Obs*R-squared 4.446607 Prob. Chi-Square(2) 0.1083
Scaled explained SS 464.5247 Prob. Chi-Square(2) 0.0000

Which one should I use? If I am using include cross terms and assume that heteroskedasticity is present, how shall I proceed with my multiple regression model.
Expecting clarifications. Thank you

[startz]

Include the cross-terms and use robust standard errors.

[Suresh]

Thank You Startz

[Suresh]

Can any one please help me in this dilemma. I have 13 variables. Eleven of them are used as dependent variables and two are common independent variables, which use independently to run 11 regression equations. However when i consider group unit root of this 13 cross sections with 41924 observation I get the following output.
Group unit root test: Summary
Series: ER_BODAL, ER_COSMO, ER_DALMBHART, ER_EXCELCO,
ER_GRAUER, ER_JINDASTEEL, ER_NCL, ER_ORI_CARB,
ER_POLYPLEX, ER_RASHTRIYA, ER_STGOBAIN,
ER_BASMAT, ER_SENSEX
Date: 02/21/17 Time: 09:34
Sample: 4/03/2001 3/31/2016
Exogenous variables: Individual effects
Automatic selection of maximum lags
Automatic lag length selection based on SIC: 0 to 1
Newey-West automatic bandwidth selection and Bartlett kernel

Cross-
Method Statistic Prob.** sections Obs
Null: Unit root (assumes common unit root process)
Levin, Lin & Chu t* 63.8800 1.0000 13 41924

Null: Unit root (assumes individual unit root process)
Im, Pesaran and Shin W-stat -121.281 0.0000 13 41924
ADF - Fisher Chi-square 1674.34 0.0000 13 41924
PP - Fisher Chi-square 592.280 0.0000 13 41925

** Probabilities for Fisher tests are computed using an asymptotic Chi-square
distribution. All other tests assume asymptotic normality.
Since null hypothesis of unit root cannot be rejected since p value of Levin, Lin & Chu t* statistics is >0.05, and the series are stationary using individual unit process assumption, the following doubts arise
1. Can i ignore the presence of unit root under assumption of common unit root since I am not using panel data and use regression individually.
2. If I ignore that will it in any way affect my regression results

Looking forward for getting help from anyone here
Regards
Suresh

[redversjones]

Is there a way to directly save the result of a white test, such as the p-value of the F-test reported in the tables? this sort of stuff is always helpful for model governance and the like.

I'm aware that one could freeze the table and extract a particular cell but assuming there's a more robust and direct way of doing it based on how that cell is populated in the first place.