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eee · 2018年06月11日

如何判断因变量是否有单位根?



Finally, Dennehy would like to confirm that nonstationarity is not a problem. To test for this he conducts Dickey–Fuller tests for a unit root on each of the time series. The results are reported in Exhibit 2.




EXHIBIT 2

RESULTS OF THE DICKEY-FULLER TESTS

Time SeriesValue of the Test StatisticStandard Errort-StatisticSignificance of t
Defective assemblies per hour0.00360.00231.5910.1123
Outside air temperature–0.4230.0724–5.8460
Assembly line speed–0.5860.043–13.5100





Q. Assuming a 5% level of significance, the most appropriate conclusion that can be drawn from the Dickey–Fuller results reported in Exhibit 2 is that the:

  1. test for a unit root is inconclusive for the dependent variable.
  2. independent variables exhibit unit roots but the dependent variable does not.
  3. dependent variable exhibits a unit root but the independent variables do not.

Solution

C is correct. The Dickey–Fuller test uses the following type of regression:

xtxt−1 = b0 + g1xt−1 + εt, Et) = 0

The null hypothesis is H0: g1 = 0 versus the alternative hypothesis Ha: g1 < 0 (a one-tail test). If g1 = 0, the time series has a unit root and is nonstationary. Thus, if the null hypothesis fails to be rejected, then the possibility exists that the time series has a unit root and is nonstationary.

Based on the t ratios and their significance levels in Exhibit 2, the null hypothesis that the coefficient is zero is rejected for both outside air temperature and assembly line speed (i.e., the independent variables). But the null hypothesis is not rejected for the dependent variable, defective assemblies per hour.


此题选项涉及因变量,我不记得如何判断因变量是否有单位根了,烦请解释一下,谢谢!


EXHIBIT 2

RESULTS OF THE DICKEY-FULLER TESTS





EXHIBIT 2

RESULTS OF THE DICKEY-FULLER TESTS



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品职辅导员_小明 · 2018年06月16日

defective assemblies per hour 是因变量Y,根据表格,Y的P是大于0.05,因此是不显著的。

那这样说把,对于Y来说是不是也可以建一个AR模型,表格给的就是三个AR模型的检验结果然后让你做判断的。

品职辅导员_小明 · 2018年06月11日

和自变量判断方法一样,比如这道题,用DF检验,检验g是否为0,自变量的P值大于0.05,所以是不显著的,因此g=0,有单位根

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