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chunty

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发表于 2012-3-27 14:52 | 只看该作者
Alexis Popov, CFA, is analyzing monthly data. Popov has estimated the model xt = b0 + b1 × xt-1 + b2 × xt-2 + et. The researcher finds that the residuals have a significant ARCH process. The best solution to this is to:
A)
re-estimate the model with generalized least squares.
B)
re-estimate the model using only an AR(1) specification.
C)
re-estimate the model using a seasonal lag.


If the residuals have an ARCH process, then the correct remedy is generalized least squares which will allow Popov to better interpret the results.

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chunty

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62#
发表于 2012-3-27 14:52 | 只看该作者
Alexis Popov, CFA, has estimated the following specification: xt = b0 + b1 × xt-1 + et. Which of the following would most likely lead Popov to want to change the model’s specification?
A)
Correlation(et, et-2) is significantly different from zero.
B)
Correlation(et, et-1) is not significantly different from zero.
C)
b0 < 0.


If correlation(et, et-2) is not zero, then the model suffers from 2nd order serial correlation. Popov may wish to try an AR(2) model. Both of the other conditions are acceptable in an AR(1) mod

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chunty

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63#
发表于 2012-3-27 14:53 | 只看该作者
Alexis Popov, CFA, wants to estimate how sales have grown from one quarter to the next on average. The most direct way for Popov to estimate this would be:
A)
an AR(1) model.
B)
a linear trend model.
C)
an AR(1) model with a seasonal lag.


If the goal is to simply estimate the dollar change from one period to the next, the most direct way is to estimate xt = b0 + b1 × (Trend) + et, where Trend is simply 1, 2, 3, ....T. The model predicts a change by the value b1 from one period to the next.

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