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Basic Question 2 of 9
Some of the following assumptions of the linear regression model are not satisfied when we work with time series. They are:
II. Independence of the errors (no serial correlation).
III. Homoscedasticity (constant variance) of the errors.
IV. Normality of the error distribution.
I. Linearity of the relationship between dependent and independent variables.
II. Independence of the errors (no serial correlation).
III. Homoscedasticity (constant variance) of the errors.
IV. Normality of the error distribution.
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Learning Outcome Statements
calculate and evaluate the predicted trend value for a time series, modeled as either a linear trend or a log-linear trend, given the estimated trend coefficients;
describe factors that determine whether a linear or a log-linear trend should be used with a particular time series and evaluate limitations of trend models;
explain the requirement for a time series to be covariance stationary and describe the significance of a series that is not stationary;
CFA® 2025 Level II Curriculum, Volume 1, Module 5.