What distinguishes first-order autoregression from second-order autoregression?

First-order autoregression is a statistical technique used to model time series data by regressing the current value of the series on its immediately preceding value. This means that the prediction for the current data point is primarily determined by the previous data point alone. Therefore, the correct answer emphasizes that first-order autoregression focuses solely on the preceding value to forecast future values.

In contrast, second-order autoregression would involve using the last two previous data points to create the forecast, encompassing a broader set of previous values. Thus, the distinction lies in the number of preceding data points utilized in the model; first-order autoregression relies exclusively on the last single observation, while second-order introduces an additional lagged observation.

Understanding this fundamental difference is critical for analysts and traders when selecting the appropriate model for time series forecasting, as it influences the model's responsiveness to trends and changes in the data.