• 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.
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