标题: Reading 13: Time-Series Analysis-LOS i 习题精选 [打印本页]
作者: 土豆妮 时间: 2011-3-3 14:58 标题: [2011]Session 3-Reading 13: Time-Series Analysis-LOS i 习题精选
Session 3: Quantitative Methods for Valuation
Reading 13: Time-Series Analysis
LOS i: Describe the characteristics of random walk processes, and contrast them to covariance stationary processes.
Given an AR(1) process represented by xt+1 = b0 + b1×xt + et, the process would not be a random walk if:
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the long run mean is b0 + b1. | |
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For a random walk, the long-run mean is undefined. The slope coefficient is one, b1=1, and that is what makes the long-run mean undefined: mean = b0/(1-b1).
作者: 土豆妮 时间: 2011-3-3 14:58
David Brice, CFA, has tried to use an AR(1) model to predict a given exchange rate. Brice has concluded the exchange rate follows a random walk without a drift. The current value of the exchange rate is 2.2. Under these conditions, which of the following would be least likely?
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The residuals of the forecasting model are autocorrelated. | |
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The forecast for next period is 2.2. | |
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The process is not covariance stationary. | |
The one-period forecast of a random walk model without drift is E(xt+1) = E(xt + et ) = xt + 0, so the forecast is simply xt = 2.2. For a random walk process, the variance changes with the value of the observation. However, the error term et = xt - xt-1 is not autocorrelated.
作者: 土豆妮 时间: 2011-3-3 14:58
Which of the following statements regarding time series analysis is least accurate?
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We cannot use an AR(1) model on a time series that consists of a random walk. | |
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If a time series is a random walk, first differencing will result in covariance stationarity. | |
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An autoregressive model with two lags is equivalent to a moving-average model with two lags. | |
An autoregression model regresses a dependent variable against one or more lagged values of itself whereas a moving average is an average of successive observations in a time series. A moving average model can have lagged terms but these are lagged values of the residual.
作者: 土豆妮 时间: 2011-3-3 14:58
A time series x that is a random walk with a drift is best described as:
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xt = b0 + b1xt ? 1 + εt. | |
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The best estimate of random walk for period t is the value of the series at (t ? 1). If the random walk has a drift component, this drift is added to the previous period’s value of the time series to produce the forecast.
作者: banban1998 时间: 2011-5-23 23:46 标题: 楼主说得不错
分享到了，手机飞信软件楼主说得不错
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