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

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

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.

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