Which one is true about an autoregressive model?

I. It is a model in which you use the statistical properties of the past behavior of a variable (its derivatives with respect to time, in some sense) to predict its behavior in the future.
II. One benefit of using an autoregressive model for forecasting is that it is not necessary to predict the future values of the explanatory variables first.
III. A random walk is a special case of auto-regressive model.

Explanation: I. An autoregressive model is one in which the independent variable is a lagged value of the dependent variable.

II. If Y_t is expressed as a function of an explanatory variable X, then it is necessary to know or forecast X_t+1 in order to forecast Y_t+1,but if Y_t is expressed as a function of Y_t-1, we need only know or forecast Y_t in order to forecast Y_t+1.

III. The random walk equation is a special case of an AR(1) model with b₀ = 0 and b₁ = 1.