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Basic Question 7 of 9
If time series data is not covariance stationary, what might be a consequence for statistical analysis?
I. Inflated p-values
II. Biased parameter estimates
III. Incorrect confidence intervals
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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.