In time-series analysis, an autoregressive model is used to predict future values based on past values. The model can be represented as: $$ Y_t = \alpha + \beta_1 Y_{t-1} + \beta_2 Y_{t-2} + ... + \beta_p Y_{t-p} + \epsilon_t $$ where: $$ Y_t $$ is the value at time t, $$ \alpha $$ is a constant, $$ \beta_i $$ are the coefficients for the lagged values, and $$ \epsilon_t $$ is the error term. Which of the following statements is true about autoregressive models?

Exam

CFA Level 2

Section

Difficulty

Quantitative Methods

Characteristics of Autoregressive Models

Very Easy Time-series Analysis Autoregressive Models

In time-series analysis, an autoregressive model is used to predict future values based on past values. The model can be represented as:

$$ Y_t = \alpha + \beta_1 Y_{t-1} + \beta_2 Y_{t-2} + ... + \beta_p Y_{t-p} + \epsilon_t $$

where:

Which of the following statements is true about autoregressive models?

An autoregressive model can only use one lagged value.

An autoregressive model predicts future values using current and past values.

An autoregressive model includes only past values of the dependent variable.

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