使用R工作室的子集

Question

如果我不希望我所有的观察结果都用R计算ADF测试，我该如何计算ADF？ 我的时间系列包含3000个观察值。现在我想计算200次第一次观察的ADF测试。我尝试以下：ur.df(x, lags=5, selectlags="AIC", type="drift", subset=1:200)从包urca，library(urca)，但我得到以下错误消息：

Error in summary(ur.df(Vstoxx, lags = 5, selectlags = "AIC", type = "drift", : 
    Fehler bei der Auswertung des Argumentes 'object' bei der Methodenauswahl 
for function 'summary': Error in ur.df(Vstoxx, lags = 5, selectlags = "AIC", type = "drift", subset = 1:200) : 
    unused argument (subset = 1:200) 

其中德国部分转化为：在该方法中选择参数“对象”的评价过程中的错误。 这里是一个小数据样本：

x 
1 14.4700 
2 14.5100 
3 14.4200 
4 13.8000 
5 13.5700 
6 12.9200 
7 13.6800 
8 14.0500 
9 13.6400 
10 13.5700 
11 13.2000 
12 13.1700 
13 13.6300 
14 14.1700 
15 13.9600 
16 14.1100 
17 13.6300 
18 13.3200 
19 12.4600 
20 12.8100 
21 12.7200 
22 12.3600 
23 12.2500 
24 12.3800 
25 11.6000 
26 11.9900 
27 11.9200 
28 12.1900 
29 12.0400 
30 11.9900 
31 12.5200 
32 12.3500 
33 13.6600 
34 13.5700 
35 13.0100 
36 13.2400 
37 13.4900 
38 13.9900 
39 13.1900 
40 12.2100 
41 12.8900 
42 12.3500 
43 12.8600 
44 12.5700 
45 11.9300 
46 11.7200 
47 12.0000 
48 12.5300 
49 13.4700 
50 12.9600 
51 13.3500 
52 12.4900 
53 14.5700 

非常感谢

Answer 1

不是添加subset=参数，你可以简单地使用索引，以子集x（见下面的示例所示）

x <- c(14.4700, 14.5100, 14.4200, 13.8000, 13.5700, 12.9200, 13.6800, 
     14.0500, 13.6400, 13.5700, 13.2000, 13.1700, 13.6300, 14.1700, 13.9600, 
     14.1100, 13.6300, 13.3200, 12.4600, 12.8100, 12.7200, 12.3600, 12.2500, 12.3800, 
     11.6000, 11.9900, 11.9200, 12.1900, 12.0400, 11.9900, 12.5200, 12.3500, 13.6600, 
     13.5700, 13.0100, 13.2400, 13.4900, 13.9900, 13.1900, 12.2100, 12.8900, 12.3500, 
     12.8600, 12.5700, 11.9300, 11.7200, 12.0000, 12.5300, 13.4700, 12.9600, 13.3500, 
     12.4900, 14.5700) 

library(urca) 

# We'll use only the 50 first elements in x 
ur.df(x[1:50], lags=5, selectlags="AIC", type="drift") 

输出：

############################################################### 
# Augmented Dickey-Fuller Test Unit Root/Cointegration Test # 
############################################################### 

The value of the test statistic is: -2.1741 2.3635