What term describes how a variable correlates with itself over time?

The term that describes how a variable correlates with itself over time is autocorrelation. This statistical concept measures the relationship between a time series and a lagged version of itself. For example, if you are analyzing stock prices, autocorrelation helps determine whether past prices influence future prices. A positive autocorrelation indicates that high values tend to follow high values while low values follow low values, whereas negative autocorrelation suggests that high values follow low values.

The other options do not apply in this context. Homoscedasticity refers to the assumption that the variance of errors in a regression model is constant across all levels of the independent variable, which is different from measuring correlation over time. Dependent and independent variables are terms related to the relationship between variables in a regression analysis, but they do not specifically address the self-correlation feature expressed through time. Therefore, autocorrelation is the appropriate term that conveys the correlation of a variable with its own past values.