Applied Econometrics - Assignment Example

causes a reduction of median housing price by 0.028 in the first variant, a reduction of 0.027 in the second variant, and a reduction of 0.027 in the third variant. In the third variable, nitrogen oxide concentrations in parts per hundred million (NOX), the coefficients are 1.479 and 1.529 in the second and third variants respectively. These figures suggest that 1 percent change in nitrogen oxide concentration increases the medium housing price by 1.479 in the second variant and by 1.529 in the third variant.

Beta coefficient is the measure of the sensitivity of the estimates in influencing the median housing price. In the estimates, the beta coefficient is the slope of the model summarized into β0, β1, β4, β3, and β2. Normally, the coefficients would imply 1 percentage change in the estimate 1 and 2 would cause an increase of 0.566 and 0.0261. However, using the beta approach, the two coefficients are below, suggesting that they are below the median housing price. 6. Suppose in model (3) I added in the variable NOX DCHAS, resulting in ln(MVi) = β0 + β1RMi + β2 ln(DISi) +β3NOXi + β4DCHAS;i + β5 NOX DCHAS +Ɛi .

How would the interpretation of Ɛ3 change in model (3) after the inclusion of this variable? What is the interpretation of Ɛ5 in this model? 9. Given that the BP and White tests yield the same conclusion regarding the presence of heteroskedasticity, does this imply that the BP test is as good as the White test? Explain your reasoning in detail. Heteroskedasticity implies to the circumstance when the variability of a variable is unequal across the range of values of a second variable that predicts it.

In this circumstance, it means that the Bp test is as good as the white test since in the presence of heteroskedasticity, it is expected to be different for variability, which is not the case. Heteroskedasticity does not necessarily imply an error, but only imply variableness, i.e. variability of a variable is unequal across the range of