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An interpretation for Heteroskedasticity for below picture

E Equation: UNTITLED Workfile: DATA ECONOMETRICS::Data_e.. View Proc Object Print Name Freeze Estimate Forecast Stats Resids Heteroskedasticity Test: Breusch-Pagan-Godfrey X F-statistic Obs R-squared Scaled explained SS 5.112724 Prob. F(4,137) 18.44402 Prob. Chi-Square(4) 37.67378 0.0007 0.0010 0.0000 Prob. Chi-Square(4) Test Equation: Dependent Variable: RESID 2 Method: Least Squares Date: 01/19/19 Time: 22:00 Sample: 2 264 Included observations: 142 Variable Coefficient Std. Error t-Statistic Prob 4.54E+08 2.09E+08 2.170543 0.0317 EDUEXPENSES 85458316 30075552 2.841455 0.0052 805579.71666856. 0.483293 0.6297 13857954 3927012. 3.528880 0.0006 VALUEADDAGRI820150.0 3834554. -0.213884 0.8310 FDINFLOWS GSAVING R-squared Adjusted R-squared S.E. of regression Sum Log likelihood 0.129887Mean dependent var 0.104483 S.D. dependent var 4.78E+08 Akaike info criterion 3.13E+19 Schwarz criterion 3036.900 Hannan-Quinn criter 2.40E+08 5.05E+08 42.84367 42.94774 42.88596 squared resid

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Here the test statistics = 18.44402

Prob Chi-Square(4) = 0.0010 which is lesser than 0.01 which suggest that we have to reject null hypothesis.

According to BP Godfrey Test:

Null Hypothesis : It is homoskedastic

Alt Hypothesis : It is Heteroskedastic

Prob Chi-Square(4) = 0.0010 which is lesser than 0.01 which suggest that we have to reject null hypothesis.

Hence We conclude that It is heteroskedastic

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