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iv. Heteroscedasticity occurs when the disturbance term in a regression model is correlated with one of the explanatory variables v. In the presence of heteroscedasticity, ordinary lease squares (OLS) is an inefficient estimation technique and this causes t tests and F tests to be invalid. vi. Heteroscedasticity can be detected with the Chow test.

iv. Heteroscedasticity occurs when the disturbance term in a regression model is correlated with one of the explanatory variables v. In the presence of heteroscedasticity, ordinary lease squares (OLS) is an inefficient estimation technique and this causes t tests and F tests to be invalid. vi. Heteroscedasticity can be detected with the Chow test.

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
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Please answer parts iv, v and vi 

Assess whether the statements in (i)-(viii) are true or false, and explain why. i. One of the assumptions that needs to hold for the process {yt} to be weakly stationary is that cov(yt,Yt-k is constant over time and depends on both t and k. ii. A white noise process is a non-stationary process for which all autocorrelations are equal to zero. iii. If our series are non-stationary, it is safe to use OLS as our estimation method. iv. Heteroscedasticity occurs when the disturbance term in a regression model is correlated with one of the explanatory variables v. In the presence of heteroscedasticity, ordinary lease squares (OLS) is an inefficient estimation technique and this causes t tests and F tests to be invalid. vi. Heteroscedasticity can be detected with the Chow test. vii. In the presence of autocorrelation, ordinary lease squares (OLS) produce unbiased estimates and hence t tests and F tests are still valid. viii. One problem with the use of a lagged dependent variable as an explanatory variable is that it always gives rise to autocorrelation.
Transcribed Image Text:Assess whether the statements in (i)-(viii) are true or false, and explain why. i. One of the assumptions that needs to hold for the process {yt} to be weakly stationary is that cov(yt,Yt-k is constant over time and depends on both t and k. ii. A white noise process is a non-stationary process for which all autocorrelations are equal to zero. iii. If our series are non-stationary, it is safe to use OLS as our estimation method. iv. Heteroscedasticity occurs when the disturbance term in a regression model is correlated with one of the explanatory variables v. In the presence of heteroscedasticity, ordinary lease squares (OLS) is an inefficient estimation technique and this causes t tests and F tests to be invalid. vi. Heteroscedasticity can be detected with the Chow test. vii. In the presence of autocorrelation, ordinary lease squares (OLS) produce unbiased estimates and hence t tests and F tests are still valid. viii. One problem with the use of a lagged dependent variable as an explanatory variable is that it always gives rise to autocorrelation.
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