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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
describe the structure of an autoregressive (AR) model of order p and calculate one- and two-period-ahead forecasts given the estimated coefficients;
explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series;
explain mean reversion and calculate a mean-reverting level;
contrast in-sample and out-of-sample forecasts and compare the forecasting accuracy of different time-series models based on the root mean squared error criterion;
CFA® 2025 Level II Curriculum, Volume 1, Module 5.