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.

Correct Answer: II and III

II: One of the assumptions of regression is that the error terms are independent. With time series data, that assumption is often violated. Positive or negative autocorrelation is common.

III: Violations of homoscedasticity make it difficult to gauge the true standard deviation of the forecast errors, usually resulting in confidence intervals that are too wide or too narrow.