June 2020
Beginner to intermediate
522 pages
9h 6m
Chinese
Content preview from R 语言经典实例（原书第 2 版）
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时间序列分析
｜
453
xts_obj <- as.xts(sample_matrix, dateFormat = "Date")[, "Close"] # vector of data
adf.test(coredata(xts_obj))
#>
#> Augmented Dickey-Fuller Test
#>
#> data: coredata(xts_obj)
#> Dickey-Fuller = -3, Lag order = 5, p-value = 0.3
#> alternative hypothesis: stationary
adf.test 函数在执行 ADF 检验之前首先对数据进行平滑。首先，它会自动移除数据的
趋势，然后对数据重新中心化，使其均值变为零。
如果你的应用不希望去趋势或重新中心化，请使用 fUnitRoots 包中的 adfTest 函数：
library(fUnitRoots)
adfTest(coredata(ts1), type = "nc")
使用 type = "nc" 时，该函数既不会对数据去趋势也不会重新中心化。使用 type =
"c" 时，函数会对数据重新中心化，但不会去趋势。
adf.test 和 adfTest 函数都允许通过参数设定一个滞后期数来明确指定函数所计算
的统计量。这些函数提供了合理的默认值，但谨慎的用户应该研究参考资料中 ADF 检
验的详细描述，以确定适合他们的时间序列的滞后期数。
14.23.4 另请参阅
urca 和 CADFtest ...
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