我有一个数据集/数据框，我在其中计算了五千家公司的每日日志回报，这些公司也是列。我想在这个数据帧上进行ADF测试。我已经找到了如何估计矢量上的ADF测试，但无法找到如何在数据帧或矩阵结构上计算它。另外，在估算公司的ADF测试时，如何省略日期栏。
structure(list(Price.Date..1. = structure(c(10961, 10962, 10963,
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11029, 11030, 11031, 11032, 11033, 11036, 11037, 11038, 11039,
11040, 11043, 11044, 11045, 11046, 11047, 11050, 11051, 11052,
11053, 11054, 11057, 11058, 11059, 11060, 11061, 11064, 11065,
11066, 11067, 11072, 11073, 11074, 11075, 11079, 11080, 11081,
11082, 11085, 11086, 11087, 11088, 11089, 11092, 11093, 11094,
11095, 11096, 11099, 11100, 11101, 11102, 11103), class = "Date"),
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答案 0 :(得分：2)
您可以使用apply为每列运行一个测试。
在this link，您可以找到apply的说明。
如果＆＃34; date＆＃34;是data.frame df的第一列，然后df[,-1]是没有＆＃34; date＆＃34;的data.frame柱：
library(tseries)
#----------------------------------------------------------------
# example data:
set.seed(1)
X <- matrix(NA,300,5)
for ( i in 1:ncol(X))
{
X[,i] <- sample(-100:100,nrow(X),replace=TRUE) / 1000
}
df <- cbind( date = as.Date("2015-01-01") + (1:nrow(X))*as.difftime(1,units="days"),
as.data.frame(X) )
#----------------------------------------------------------------
a = "stationary"
lagOrder = trunc((nrow(df)-1)^(1/3))
Test <- apply(df[,-1],2,adf.test, alternative=a, k=lagOrder )
结果：
> Test <- apply(df[,-1],2,adf.test, alternative=a, k=lagOrder )
Warning messages:
1: In FUN(newX[, i], ...) : p-value smaller than printed p-value
2: In FUN(newX[, i], ...) : p-value smaller than printed p-value
3: In FUN(newX[, i], ...) : p-value smaller than printed p-value
4: In FUN(newX[, i], ...) : p-value smaller than printed p-value
5: In FUN(newX[, i], ...) : p-value smaller than printed p-value
> Test
$V1
Augmented Dickey-Fuller Test
data: newX[, i]
Dickey-Fuller = -6.9796, Lag order = 6, p-value = 0.01
alternative hypothesis: stationary
$V2
Augmented Dickey-Fuller Test
data: newX[, i]
Dickey-Fuller = -6.6985, Lag order = 6, p-value = 0.01
alternative hypothesis: stationary
$V3
Augmented Dickey-Fuller Test
data: newX[, i]
Dickey-Fuller = -6.5085, Lag order = 6, p-value = 0.01
alternative hypothesis: stationary
$V4
Augmented Dickey-Fuller Test
data: newX[, i]
Dickey-Fuller = -6.9839, Lag order = 6, p-value = 0.01
alternative hypothesis: stationary
$V5
Augmented Dickey-Fuller Test
data: newX[, i]
Dickey-Fuller = -7.0185, Lag order = 6, p-value = 0.01
alternative hypothesis: stationary
我不确定警告的来源。也许我的示例数据不合理。 Test[n]是对第n家公司的测试。
列NA中缺少值（x）可能是个问题。如果x被na.omit(x)替换，则时间序列不再等距。一个想法是通过插值填充NA - 间隙。在以下示例中，我们使用线性插值：
library(tseries)
#----------------------------------------------------------------
# example data:
set.seed(1)
X <- matrix(NA,300,5)
for ( i in 1:ncol(X))
{
X[,i] <- sample(-100:100,nrow(X),replace=TRUE) / 1000
}
for ( i in 1:ncol(X))
{
X[sample(1:nrow(X),floor(nrow(X)/10),replace=FALSE),i] <- NA
}
df <- cbind( date = as.Date("2015-01-01") + (1:nrow(X))*as.difftime(1,units="days"),
as.data.frame(X) )
#----------------------------------------------------------------
a = "stationary"
lagOrder = trunc((nrow(df)-1)^(1/3))
Test <- apply(df[,-1], 2,
function(x){
tryCatch(
{
n <- which.max(!is.na(x))
m <- nrow(df)-which.max(!is.na(rev(x)))+1
return(adf.test(approx(n:m,x[n:m],xout=n:m)$y, alternative=a, k=lagOrder ))
},
error = function(e)
{
message(e)
writeLines("")
return(NA)
} )
}
)
缺少值的示例数据：
> head(df,15)
date V1 V2 V3 V4 V5
1 2015-01-02 -0.047 NA 0.063 0.067 -0.027
2 2015-01-03 -0.026 -0.081 0.086 NA 0.049
3 2015-01-04 0.015 -0.001 -0.071 -0.046 NA
4 2015-01-05 0.082 -0.008 0.050 -0.063 0.035
5 2015-01-06 -0.060 -0.025 0.096 -0.055 0.040
6 2015-01-07 0.080 0.099 0.095 -0.088 0.070
7 2015-01-08 NA -0.065 -0.030 -0.088 0.041
8 2015-01-09 0.032 0.063 -0.021 -0.071 0.072
9 2015-01-10 0.026 -0.087 0.091 -0.086 -0.011
10 2015-01-11 -0.088 -0.020 -0.079 NA NA
11 2015-01-12 -0.059 -0.072 0.087 0.005 -0.075
12 2015-01-13 -0.065 -0.062 -0.031 NA 0.047
13 2015-01-14 0.038 0.069 0.007 0.039 NA
14 2015-01-15 -0.023 NA NA NA NA
15 2015-01-16 0.054 -0.047 0.043 -0.097 -0.018
结果：
> Test
$V1
Augmented Dickey-Fuller Test
data: approx(n:m, x[n:m], xout = n:m)$y
Dickey-Fuller = -6.5244, Lag order = 6, p-value = 0.01
alternative hypothesis: stationary
$V2
Augmented Dickey-Fuller Test
data: approx(n:m, x[n:m], xout = n:m)$y
Dickey-Fuller = -6.4918, Lag order = 6, p-value = 0.01
alternative hypothesis: stationary
$V3
Augmented Dickey-Fuller Test
data: approx(n:m, x[n:m], xout = n:m)$y
Dickey-Fuller = -6.519, Lag order = 6, p-value = 0.01
alternative hypothesis: stationary
$V4
Augmented Dickey-Fuller Test
data: approx(n:m, x[n:m], xout = n:m)$y
Dickey-Fuller = -7.2095, Lag order = 6, p-value = 0.01
alternative hypothesis: stationary
$V5
Augmented Dickey-Fuller Test
data: approx(n:m, x[n:m], xout = n:m)$y
Dickey-Fuller = -7.2067, Lag order = 6, p-value = 0.01
alternative hypothesis: stationary
>
使用问题中给出的数据的示例：
> a = "stationary"
> lagOrder = trunc((nrow(df)-1)^(1/3))
> Test <- apply(df[,-1], 2,
+ function(x){
+ tryCatch(
+ {
+ n <- which.max(!is.na .... [TRUNCATED]
need at least two non-NA values to interpolate
need at least two non-NA values to interpolate
need at least two non-NA values to interpolate
need at least two non-NA values to interpolate
Warning messages:
1: In adf.test(approx(n:m, x[n:m], xout = n:m)$y, alternative = a, :
p-value smaller than printed p-value
2: In adf.test(approx(n:m, x[n:m], xout = n:m)$y, alternative = a, :
p-value smaller than printed p-value
3: In adf.test(approx(n:m, x[n:m], xout = n:m)$y, alternative = a, :
p-value smaller than printed p-value
4: In adf.test(approx(n:m, x[n:m], xout = n:m)$y, alternative = a, :
p-value smaller than printed p-value
5: In adf.test(approx(n:m, x[n:m], xout = n:m)$y, alternative = a, :
p-value smaller than printed p-value
>
结果：
> Test
$A.G.L.SJ.INVS...LON..DEAD...13.08.15...S.
Augmented Dickey-Fuller Test
data: approx(n:m, x[n:m], xout = n:m)$y
Dickey-Fuller = -4.4222, Lag order = 4, p-value = 0.01
alternative hypothesis: stationary
$ABACUS.GROUP.DEAD...18.02.09...S.
Augmented Dickey-Fuller Test
data: approx(n:m, x[n:m], xout = n:m)$y
Dickey-Fuller = -6.4671, Lag order = 4, p-value = 0.01
alternative hypothesis: stationary
$ABB.R..IRS....S.
[1] NA
$ABBEY.NATIONAL.DEAD...T.O.SEE.702853...S.
Augmented Dickey-Fuller Test
data: approx(n:m, x[n:m], xout = n:m)$y
Dickey-Fuller = -4.4337, Lag order = 4, p-value = 0.01
alternative hypothesis: stationary
$ABBEY.PROTECTION.DEAD...20.01.14...S.
[1] NA
$ABBEYCREST.DEAD...10.10.14...S.
Augmented Dickey-Fuller Test
data: approx(n:m, x[n:m], xout = n:m)$y
Dickey-Fuller = -5.7007, Lag order = 4, p-value = 0.01
alternative hypothesis: stationary
$ABBOT.GROUP.DEAD...07.03.08...S.
Augmented Dickey-Fuller Test
data: approx(n:m, x[n:m], xout = n:m)$y
Dickey-Fuller = -4.5546, Lag order = 4, p-value = 0.01
alternative hypothesis: stationary
$ABBOTT.LABS.GBP..LON..DEAD...DEAD...S.
[1] NA
$ABERDEEN.ASSET.MAN..FULLY.PAID.23.09.05...S.
[1] NA
> which(is.na(Test))
ABB.R..IRS....S.
3
ABBEY.PROTECTION.DEAD...20.01.14...S.
5
ABBOTT.LABS.GBP..LON..DEAD...DEAD...S.
8
ABERDEEN.ASSET.MAN..FULLY.PAID.23.09.05...S.
9
>