Explanation:

Lag operators (also known as backshift operators) are mathematical operators used in time series analysis to represent the relationship between current and past values of a time series. The key characteristics of lag operators are:

1. They quantify how a time series evolves by lagging a data series - This is the correct statement. Lag operators shift a time series backward in time, allowing us to express current values as functions of past values.

2. They use lagged past values - Option A is incorrect because lag operators use lagged past values, not future values. The notation L (or B) applied to a time series y_t gives y_{t-1}.

3. They are widely useful in time series modeling - Option B is incorrect because lag operators are fundamental tools in ARIMA, ARMA, and other time series models, not of limited use.

4. They can use both finite and infinite-order polynomials - Option C is incorrect because lag operators can be used with both finite-order polynomials (as in AR models) and infinite-order polynomials (as in MA models or when expressing AR processes as infinite MA processes).

Mathematical Representation:

• L(y_t) = y_{t-1}
• L^k(y_t) = y_{t-k}

Lag operators are essential for expressing autoregressive (AR) and moving average (MA) models compactly, and they help in analyzing stationarity, invertibility, and other properties of time series models.