[问答] 单位根检验，同包不同方法下与同方法不同包下的相反结果 [推广有奖]

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huangruiji110 发表于 2012-4-29 15:47:36 |AI写论文

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对于单位根检验，最常用的方法是ADF检验，另外还有PP检验、KPSS检验，
而R中用于单位根检验的包最常见的是｛tseries｝，另外还有个｛urca｝包，其报告结果更加详细。

library(tseries)
adf.test(price) ## null hypothesis is that x has a unit root
pp.test(price) ## null hypothesis is that x has a unit root
kpss.test(price) ## Null hypothesis is stationarity
library(urca)
summary(ur.df(price)) ## null hypothesis is that x has a unit root
summary(ur.pp(price)) ## null hypothesis is that x has a unit root
summary(ur.kpss(price)) ## Null hypothesis is stationarity

复制代码

报告结果如下：
————————————————————————————————————————————————
> library(tseries)
> adf.test(price)             ## null hypothesis is that x has a unit root

      Augmented Dickey-Fuller Test

data:  price
Dickey-Fuller = -3.5781, Lag order = 6, p-value = 0.03575
alternative hypothesis: stationary

> pp.test(price)             ## null hypothesis is that x has a unit root

      Phillips-Perron Unit Root Test

data:  price
Dickey-Fuller Z(alpha) = -18.6334, Truncation lag parameter = 5,
p-value = 0.09041
alternative hypothesis: stationary

> kpss.test(price)          ## Null hypothesis is stationarity

      KPSS Test for Level Stationarity

data:  price
KPSS Level = 3.9207, Truncation lag parameter = 3, p-value = 0.01

警告信息：
In kpss.test(price) : p-value smaller than printed p-value
>
> library(urca)
> summary(ur.df(price))       ## null hypothesis is that x has a unit root

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

Test regression none

Call:
lm(formula = z.diff ~ z.lag.1 - 1 + z.diff.lag)

Residuals:
   Min    1Q Median    3Q    Max
-1053.53 -68.74 14.88 93.19 792.42

Coefficients:
         Estimate Std. Error t value Pr(>|t|)
z.lag.1 -0.003181 0.006969  -0.456 0.648
z.diff.lag -0.026130 0.063709  -0.410 0.682

Residual standard error: 212.3 on 248 degrees of freedom
Multiple R-squared: 0.001649, Adjusted R-squared: -0.006402
F-statistic: 0.2048 on 2 and 248 DF,  p-value: 0.8149

Value of test-statistic is: -0.4564

Critical values for test statistics:
   1pct  5pct 10pct
tau1 -2.58 -1.95 -1.62

> summary(ur.pp(price))       ## null hypothesis is that x has a unit root

##################################
# Phillips-Perron Unit Root Test #
##################################

Test regression with intercept

Call:
lm(formula = y ~ y.l1)

Residuals:
Min    1Q  Median    3Q    Max
-984.02  -87.12 -9.17 76.04  809.96

Coefficients:
         Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.76566 24.94151 1.955 0.0517 .
y.l1       0.97519 0.01293  75.433 <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 210.3 on 249 degrees of freedom
Multiple R-squared: 0.9581,    Adjusted R-squared: 0.9579
F-statistic:  5690 on 1 and 249 DF,  p-value: < 2.2e-16

Value of test-statistic, type: Z-alpha  is: -8.5727

      aux. Z statistics
Z-tau-mu          2.1364

> summary(ur.kpss(price))    ## Null hypothesis is stationarity

#######################
# KPSS Unit Root Test #
#######################

Test is of type: mu with 5 lags.

Value of test-statistic is: 2.6757

Critical value for a significance level of:
            10pct  5pct 2.5pct  1pct
critical values 0.347 0.463  0.574 0.739
———————————————————————————————————————————
检验结果报告：