13: Time-Series Analysis-LOS d, (Part 1)习题精选 style, center, structure, 2010

土豆妮

Session 3: Quantitative Methods: Quantitative
Methods for Valuation
Reading 13: Time-Series Analysis

LOS d, (Part 1): Discuss the structure of an autoregressive (AR) model of order p.

 

 

 

An analyst wants to model quarterly sales data using an autoregressive model. She has found that an AR(1) model with a seasonal lag has significant slope coefficients. She also finds that when the second and third lags are added to the model, all slope coefficients are significant too. Based on this, the best model to use would most likely be an:

A) AR(1).

B) AR(4).

C) AR(2).

土豆妮

An analyst wants to model quarterly sales data using an autoregressive model. She has found that an AR(1) model with a seasonal lag has significant slope coefficients. She also finds that when the second and third lags are added to the model, all slope coefficients are significant too. Based on this, the best model to use would most likely be an:

A) AR(1).

B) AR(4).

C) AR(2).

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She has found that all the slope coefficients are significant in the model x_t = b₀ + b₁x_t–1 + b₂x_t–4 + e_t. She then finds that all the slope coefficients are significant in the model x_t = b₀ + b₁x_t–1 + b₂x_t–2 + b₃x_t–3 + b₄x_t–4 + e_t. Thus, the second model, the AR(4), should be used over the first or any other model that uses a subset of the regressors.

土豆妮

The model x_t = b₀ + b₁ x_t-1 + b₂ x_t-2 + b₃ x_t-3 + b₄ x_t-4 + ε_t is:

A) an autoregressive conditional heteroskedastic model, ARCH.

B) a moving average model, MA(4).

C) an autoregressive model, AR(4).

土豆妮

The model x_t = b₀ + b₁ x_t-1 + b₂ x_t-2 + b₃ x_t-3 + b₄ x_t-4 + ε_t is:

A) an autoregressive conditional heteroskedastic model, ARCH.

B) a moving average model, MA(4).

C) an autoregressive model, AR(4).

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This is an autoregressive model (i.e., lagged dependent variable as independent variables) of order p=4 (that is, 4 lags).

土豆妮

The model x_t = b₀ + b₁ x_{t ? 1} + b₂ x_{t ? 2}  + ε_t is:

A) an autoregressive conditional heteroskedastic model, ARCH.

B) an autoregressive model, AR(2).

C) a moving average model, MA(2).

土豆妮

The model x_t = b₀ + b₁ x_{t ? 1} + b₂ x_{t ? 2}  + ε_t is:

A) an autoregressive conditional heteroskedastic model, ARCH.

B) an autoregressive model, AR(2).

C) a moving average model, MA(2).

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This is an autoregressive model (i.e., lagged dependent variable as independent variables) of order p = 2 (that is, 2 lags).

luqian55

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annyyu

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