• CIGS: average number of cigarettes smoked by the mother per day
• MAGE: age of the mother (in years)
• MALE: 1 if the child is a male, 0 otherwise
• NPVIS: total number of prenatal visits

TABLE 3
Dependent Variable: RESID^2
Variable Coefficient Std. Error
Constant 1.797312 2.044727
CIGS 0.029079 0.032139
CIGS^2 7.82E-05 0.000127
CIGS*MAGE -0.002281 0.002237
CIGS*(MAGE^2) 3.73E-05 3.81E-05
CIGS*MALE 0.000682 0.002298
CIGS*NPVIS 0.000168 0.000205
MAGE -0.266978 0.288828
MAGE^2 0.014901 0.015135
MAGE*(MAGE^2) -0.000337 0.000346
MAGE*MALE 0.003528 0.018641
MAGE*NPVIS -0.004875 0.002625
(MAGE^2)^2 2.62E-06 2.91E-06
(MAGE^2)*MALE -3.66E-05 0.000313
(MAGE^2)*NPVIS 8.25E-05 4.41E-05
MALE -0.067026 0.274525
MALE*NPVIS -0.000336 0.002541
NPVIS 0.050867 0.038389
NPVIS^2 0.000477 0.000115
R-squared 0.026298

- We estimate the regression model for the squared residuals (RESID ̂2) shown in the Table 3. How would you implement a test for heteroskedasticity based on this regression results?
- State the null and alternative hypotheses, and calculate the X2 test (use a 2.5% significance level in your decision).
- Based on the results of the White tests, do you think that OLS is the appropriate estimator? What goes wrong if we use OLS and the errors are heteroskedastic?
- What remedies are possible if you find evidence of heteroskedasticity?
- What other tests you can recommend to test heteroskedasticity? Explain how you can run one of the tests.

Optional Information:
Level/Year: BA/4th
Subject: Econmetrics/Stats

Already Tried:
internet/notes, textbook