Consider the linear model to explain monthly beer consumption: where E(u|inc,pri
ID: 1244685 • Letter: C
Question
Consider the linear model to explain monthly beer consumption:
where E(u|inc,price, educ, female) = 0 and Var(u|inc, price, educ, female) = ?2inc2
1. What is heteroskedasticity? Explain.
2. Write the transformed model that has an homoskedastic error term.
3. Outline the intuition behind the BP and White tests for heteroskedasticity.
4. If we have dummy variables in our model (e.g., female), we will get an error when usingthe White test. Why does this happen? How can we get around this problem?
Consider the linear model to explain monthly beer consumption: where E(u|inc,price, educ, female) = 0 and Var(u|inc, price, educ, female) = ?2inc2 1. What is heteroskedasticity? Explain. 2. Write the transformed model that has an homoskedastic error term. 3. Outline the intuition behind the BP and White tests for heteroskedasticity. 4. If we have dummy variables in our model (e.g., female), we will get an error when usingthe White test. Why does this happen? How can we get around this problem?Explanation / Answer
1. Heteroskedasticity is the property that violates the assumption of the classical linear regression model. The classical linear regression model assumes that the variance of the error term is constant but if we have different sets of data such as say data on exports of different countries which is likely to vary a lot as exports will be different in different countries. This brings in the problem of Heteroskedasticity as the variance is now no longer constant and it changes with the change in the country. since var(u) = sigma2 inc2 then a simple transformation would be to divide the entire equation be (inc) and then estimate the model. 3. the intuition behind these tests is simple. when one tries to estimate the variance of error term we know that the error term is independent of the explanatory variable. thus what we do in these tests iswe estimate the error term from the original model and then regress it against the explanatory variable. a F test result is checked in this case. if it is significantthen we can assume that the variables jointly have a significant impact on the error term and the null hypothesis of no heteroskedasticity can be rejected. 4. If any of the original explanatory variable is a dummy variable, then its square will be identical to the original, and they will correlate perfectly hence the white test gives an error in such cases. please do rate me. Require ratings. Thank you