Reading 13: Time-Series Analysis-LOS c 习题精选 2011, center, process, discuss, following

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

LOS c: Explain the requirement for a time series to be covariance stationary, and discuss the significance of a series that is not stationary.

 

 

To qualify as a covariance stationary process, which of the following does not have to be true?

A) Covariance(xt, xt-1) = Covariance(xt, xt-2).

B) E[xt] = E[xt+1].

C) Covariance(xt, xt-2) = Covariance(xt, xt+2).

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If a series is covariance stationary then the unconditional mean is constant across periods. The unconditional mean or expected value is the same from period to period: E[x_t] = E[x_t+1]. The covariance between any two observations equal distance apart will be equal, e.g., the t and t-2 observations with the t and t+2 observations. The one relationship that does not have to be true is the covariance between the t and t-1 observations equaling that of the t and t-2 observations.

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Which of the following is NOT a requirement for a series to be covariance stationary? The:

A) expected value of the time series is constant over time.

B) covariance of the time series with itself (lead or lag) must be constant.

C) time series must have a positive trend.

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A time series can be covariance stationary and have either a positive or a negative trend.

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Which of the following statements regarding covariance stationarity is CORRECT?

A) A time series that is covariance stationary may have residuals whose mean changes over time.

B) A time series may be both covariance stationary and have heteroskedastic residuals.

C) The estimation results of a time series that is not covariance stationary are meaningless.

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Covariance stationarity requires that the expected value and the variance of the time series be constant over time.