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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:

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.