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简ying · 2020年12月09日

问一道题:NO.PZ201709270100000509 第9小题 [ CFA II ]

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问题如下:

9.Based on Exhibit 5, which single time-series model would most likely be appropriate for Busse to use in predicting the future stock price of Company #3?

选项:

A.

Log-linear trend model

B.

First-differenced AR(2) model

C.

First-differenced log AR(1) model

解释:

C is correct. As a result of the exponential trend in the time series of stock prices for Company #3, Busse would want to take the natural log of the series and then first-difference it. Because the time series also has serial correlation in the residuals from the trend model, Busse should use a more complex model, such as an autoregressive (AR) model.

想问一下,课件里哪里讲到如果时间序列有serial correlation就用AR解决啊?
1 个答案

星星_品职助教 · 2020年12月09日

同学你好,

在多元回归模型中应用时间序列数据经常会产生大量的serial correlation的问题。这种问题的原因经常是因为模型不合适,数据中体现了“昨天的我可以解释今天的我”的特点,但模型中体现不出来。

在这种情况下,用AR模型来取代多元回归就可以把这方面的serial correlation问题解决。

但AR并不是解决serial correlation的通用方法,只是说在这种特定条件下可以解决,或者说这个时候AR是更适合的模型。

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