Homework Help is Here – Start Your Trial Now!
Math
Statistics
Consider the following regression equation: Y = Bo +P,X;+H. where X,, Y. Po B, and u, denote the regressor, the regressand, the intercept coefficient, the slope coefficient, and the error term for the observation, respectively When would the error term be homoskedastic? O A. The error term is homoskedastic if the variance of the conditional distribution of u. given X is constant for i=1n and in particular does not depend on X, O B. The error term is homoskedastic if the variance of the joint distribution of u, and Y, is constant for i= 1.n, and in particular does not depend on Y O C. The error term is homoskedastic if the variance of the conditional distribution of u given Y is constant for i=1n, and in particular does not depend on Y O D. The error term is homoskedastic if the variance of the conditional distribution of u given X, is variable for i=1n, and in particular depends on X Which of the following statements describes the mathematical implications of heteroskedasticity? O A. The OLS estimators remain unbiased, consistent and have the least variance among all estimators that are linear in Y, Y. conditional on X X but they are not asyn O B. The OLS estimators remain consistent, asymptotically normal and have the least variance among all estimators that are linear in Y, Y conditional on X,X, but they O C. The OLS estimators remain unbiased, consistent, asymptotically normal, but do not necessarily have the least variance among all estimators that are linear in Y, Y, condi O D. The OLS estimators remain unbiased, asymptotically normal and have the least variance among all estimators that are linear in Y, Y conditional on X, X, but they a If the errors are heteroskedastic, then the t-statistic computed using v standard error does not have a standard normal distribution, even in large samples. homoskedasticity-only Click to select your answer. heteroskedasticity-robust

Consider the following regression equation: Y = Bo +P,X;+H. where X,, Y. Po B, and u, denote the regressor, the regressand, the intercept coefficient, the slope coefficient, and the error term for the observation, respectively When would the error term be homoskedastic? O A. The error term is homoskedastic if the variance of the conditional distribution of u. given X is constant for i=1n and in particular does not depend on X, O B. The error term is homoskedastic if the variance of the joint distribution of u, and Y, is constant for i= 1.n, and in particular does not depend on Y O C. The error term is homoskedastic if the variance of the conditional distribution of u given Y is constant for i=1n, and in particular does not depend on Y O D. The error term is homoskedastic if the variance of the conditional distribution of u given X, is variable for i=1n, and in particular depends on X Which of the following statements describes the mathematical implications of heteroskedasticity? O A. The OLS estimators remain unbiased, consistent and have the least variance among all estimators that are linear in Y, Y. conditional on X X but they are not asyn O B. The OLS estimators remain consistent, asymptotically normal and have the least variance among all estimators that are linear in Y, Y conditional on X,X, but they O C. The OLS estimators remain unbiased, consistent, asymptotically normal, but do not necessarily have the least variance among all estimators that are linear in Y, Y, condi O D. The OLS estimators remain unbiased, asymptotically normal and have the least variance among all estimators that are linear in Y, Y conditional on X, X, but they a If the errors are heteroskedastic, then the t-statistic computed using v standard error does not have a standard normal distribution, even in large samples. homoskedasticity-only Click to select your answer. heteroskedasticity-robust

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
See similar textbooks
icon
Related questions
icon
Concept explainers
Question
100%
Consider the following regression equation: Y, = Po + B,X, + Hip where X;, Y, þPo, B1, and u, denote the regressor, the regressand, the intercept coefficient, the slope coefficient, and the error term for the " observation, respectively. When would the error term be homoskedastic? A. The error term is homoskedastic if the variance of the conditional distribution of u, given X is constant for i= 1..n, and in particular does not depend on X, B. The error term is homoskedastic if the variance of the joint distribution of u, and Y, is constant for i= 1. n and in particular does not depend on Y- O C. The error term is homoskedastic if the variance of the conditional distribution of u, given Y is constant for i= 1.n, and in particular does not depend on Y O D. The error term is homoskedastic if the variance of the conditional distribution of u given X, is variable for i=1. n, and in particular depends on Xj. Which of the following statements describes the mathematical implications of heteroskedasticity? A. The OLS estimators remain unbiased, consistent and have the least variance among all estimators that are linear in Y,, Y, conditional on X,, X, but they are not asymptotically normal. B. The OLS estimators remain consistent, asymptotically normal and have the least variance among all estimators that are linear in Y,, Yn, conditional on X, X, but they are not unbiased O C. The OLS estimators remain unbiased, consistent, asymptotically normal, but do not necessarily have the least variance among all estimators that are linear in Y, Y, conditional on X1, D. The OLS estimators remain unbiased, asymptotically normal and have the least variance among all estimators that are linear in Y, Y conditional on X X but they are not consistent. If the errors are heteroskedastic, then the t-statistic computed using V standard error does not have a standard normal distribution, even in large samples. homoskedasticity-only Click to select your answer. heteroskedasticity-robust P Type here to search DELL
Transcribed Image Text:Consider the following regression equation: Y, = Po + B,X, + Hip where X;, Y, þPo, B1, and u, denote the regressor, the regressand, the intercept coefficient, the slope coefficient, and the error term for the " observation, respectively. When would the error term be homoskedastic? A. The error term is homoskedastic if the variance of the conditional distribution of u, given X is constant for i= 1..n, and in particular does not depend on X, B. The error term is homoskedastic if the variance of the joint distribution of u, and Y, is constant for i= 1. n and in particular does not depend on Y- O C. The error term is homoskedastic if the variance of the conditional distribution of u, given Y is constant for i= 1.n, and in particular does not depend on Y O D. The error term is homoskedastic if the variance of the conditional distribution of u given X, is variable for i=1. n, and in particular depends on Xj. Which of the following statements describes the mathematical implications of heteroskedasticity? A. The OLS estimators remain unbiased, consistent and have the least variance among all estimators that are linear in Y,, Y, conditional on X,, X, but they are not asymptotically normal. B. The OLS estimators remain consistent, asymptotically normal and have the least variance among all estimators that are linear in Y,, Yn, conditional on X, X, but they are not unbiased O C. The OLS estimators remain unbiased, consistent, asymptotically normal, but do not necessarily have the least variance among all estimators that are linear in Y, Y, conditional on X1, D. The OLS estimators remain unbiased, asymptotically normal and have the least variance among all estimators that are linear in Y, Y conditional on X X but they are not consistent. If the errors are heteroskedastic, then the t-statistic computed using V standard error does not have a standard normal distribution, even in large samples. homoskedasticity-only Click to select your answer. heteroskedasticity-robust P Type here to search DELL
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 4 steps

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Knowledge Booster
Correlation, Regression, and Association
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.
Similar questions
Recommended textbooks for you
MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman
SEE MORE TEXTBOOKS