README.md

In mlincon/pvurt: P-value for augmented Dickey-Fuller unit root test

pvurt

This package has been created as part of my master thesis. The package has only one user facing function: computePVale(). The function approximates p-value (cumulative probability) for augmented Dickey-Fuller unit root test based on the distribution of the test statistics. The approximation is based on pre-trained generalized additive logistic model or logistic regression with polynomial terms.

The function can take either a numeric value or the output from the four functions of the following three packages: adfTest() and unitrootTest() from fUnitRoots, adf.test() from tseries and, lastly, ur.df() from urca. If output from any of the function is passed on, computePVale() appends its approximation result to the original output.

Installation

The package has not been released in CRAN. To install use:

devtools::install_github("mlincon/pvurt")

Since the package must be complied, ensure that Rtools.exe is installed beforehand.

Example

Incorporating pvurt in existing script is quite straightforward and amounts to simply passing the output from the other functions to computePValue() or simply chaining it using the pipe (%>%) operator.

library(pvurt)

y <- arima.sim(model = list(order = c(0, 1, 0)), n = 100)

# Test type: with drift and trend
# package: fUnitRoots
library(fUnitRoots)
#> Loading required package: timeDate
#> Loading required package: timeSeries
#> Loading required package: fBasics
computePValue(adfTest(y, lags = 3, type = "ct"))
#> 
#> Title:
#>  Augmented Dickey-Fuller Test
#> 
#> Test Results:
#>   PARAMETER:
#>     Lag Order: 3
#>   STATISTIC:
#>     Dickey-Fuller: -1.1702
#>   P VALUE:
#>     t: 0.9078 
#>     pvurt: 0.9099 
#> 
#> Description:
#>  Fri Sep 13 11:18:55 2019 by user: User

computePValue(unitrootTest(y, lags = 3, type = "ct"))
#> 
#> Title:
#>  Augmented Dickey-Fuller Test
#> 
#> Test Results:
#>   PARAMETER:
#>     Lag Order: 3
#>   STATISTIC:
#>     DF: -1.1702
#>   P VALUE:
#>     t: 0.9107 
#>     n: 0.9851 
#>     pvurt: 0.9099 
#> 
#> Description:
#>  Fri Sep 13 11:18:55 2019 by user: User

# package: urca
library(urca)
#> 
#> Attaching package: 'urca'
#> The following objects are masked from 'package:fUnitRoots':
#> 
#>     punitroot, qunitroot, unitrootTable
computePValue(ur.df(y, lags = 3, type = "trend"))
#> 
#> ############################################################### 
#> # Augmented Dickey-Fuller Test Unit Root / Cointegration Test # 
#> ############################################################### 
#> 
#> The value of the test statistic is: -1.1702 0.5717 0.8238 
#> Approximated P value is (pvurt):    0.9099 NA NA

# or with pipe operator
ur.df(lc, lags = 3, type = "trend") %>% computePValue()

# print summary
summary(computePValue(ur.df(y, lags = 3, type = "trend")))
#> 
#> ############################################### 
#> # Augmented Dickey-Fuller Test Unit Root Test # 
#> ############################################### 
#> 
#> Test regression trend 
#> 
#> 
#> Call:
#> lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -2.51378 -0.61868  0.01927  0.54949  2.15283 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)
#> (Intercept)  0.017396   0.205271   0.085    0.933
#> z.lag.1     -0.036016   0.030778  -1.170    0.245
#> tt           0.001004   0.003939   0.255    0.799
#> z.diff.lag1  0.021370   0.105427   0.203    0.840
#> z.diff.lag2  0.051329   0.105211   0.488    0.627
#> z.diff.lag3 -0.013270   0.105714  -0.126    0.900
#> 
#> Residual standard error: 0.879 on 91 degrees of freedom
#> Multiple R-squared:  0.01995,    Adjusted R-squared:  -0.0339 
#> F-statistic: 0.3705 on 5 and 91 DF,  p-value: 0.8677
#> 
#> 
#> Value of test-statistic is:     -1.1702 0.5717 0.8238 
#> Approximated P value is (pvurt): 0.9099 NA NA 
#> 
#> Critical values for test statistics: 
#>       1pct  5pct 10pct
#> tau3 -3.99 -3.43 -3.13
#> phi2  6.22  4.75  4.07
#> phi3  8.43  6.49  5.47

# package: tseries
library(tseries)
computePValue(adf.test(y, alternative = "stationary", k = 3))
#> 
#>  Augmented Dickey-Fuller Test
#> 
#> data:  y
#> Dickey-Fuller = -1.17, Lag order = 3, p-value = 0.9078, pvurt =
#> 0.9099
#> alternative hypothesis: stationary

# no packages
tStat <- -2.239
sampleSize <- 100
computePValue(tStat, n = sampleSize, model = "gam", type = "ct")
#> [1] 0.4626582

mlincon/pvurt documentation built on June 8, 2021, 4:14 p.m.