Suppose that the regression model is yi = + i, where E[i | xi ] =
Question:
Suppose that the regression model is yi = μ + εi, where E[εi | xi ] = 0, Cov[εi, εj | xi , xj] = 0 for i ≠ j , but Var[εi | xi] = σ2x2i , xi > 0.
a. Given a sample of observations on yi and xi, what is the most efficient estimator of μ? What is its variance?
b. What is the OLS estimator of μ, and what is the variance of the ordinary least squares estimator?
c. Prove that the estimator in part a is at least as efficient as the estimator in part b.
Fantastic news! We've Found the answer you've been seeking!
Step by Step Answer:
This is a heteroscedastic regression model in which the matrix X is a column of o...View the full answer
Answered By
Anthony Ngatia
I have three academic degrees i.e bachelors degree in Education(English & Literature),bachelors degree in business administration(entrepreneurship option),and masters degree in business administration(strategic management) in addition to a diploma in business management.I have spent much of my life in the academia where I have taught at high school,middle level colleges level and at university level.I have been an active academic essays writer since 2011 where I have worked with some of the most reputable essay companies based in Europe and in the US.I have over the years perfected my academic writing skills as a result of tackling numerous different assignments.I do not plagiarize and I maintain competitive quality in all the assignments that I handle.I am driven by strong work ethics and a firm conviction that I should "Do Unto others as I would Like them to do to me".
76+ Reviews
152+ Question Solved
Related Book For 
Question Posted:
