adf {bootUR}R Documentation

Augmented Dickey-Fuller Unit Root Test

Description

This function performs a standard augmented Dickey-Fuller unit root test on a single time series.

Usage

adf(data, data_name = NULL, deterministics = "intercept", min_lag = 0,
  max_lag = NULL, criterion = "MAIC", criterion_scale = TRUE,
  two_step = TRUE)

Arguments

data

A T-dimensional vector to be tested for unit roots. Data may also be in a time series format (e.g. ts, zoo or xts), or a data frame.

data_name

Optional name for the data, to be used in the output. The default uses the name of the 'data' argument.

deterministics

String indicating the deterministic specification. Only relevant if union = FALSE. Options are

"none": no deterministics;

"intercept": intercept only;

"trend": intercept and trend.

If union = FALSE, the default is adding an intercept (a warning is given).

min_lag

Minimum lag length in the augmented Dickey-Fuller regression. Default is 0.

max_lag

Maximum lag length in the augmented Dickey-Fuller regression. Default uses the sample size-based rule 12(T/100)^{1/4}.

criterion

String for information criterion used to select the lag length in the augmented Dickey-Fuller regression. Options are: "AIC", "BIC", "MAIC", "MBIC". Default is "MAIC" (Ng and Perron, 2001).

criterion_scale

Logical indicator whether or not to use the rescaled information criteria of Cavaliere et al. (2015) (TRUE) or not (FALSE). Default is TRUE.

two_step

Logical indicator whether to use one-step (two_step = FALSE) or two-step (two_step = TRUE) detrending. The default is two-step detrending.

Details

The function encompasses the standard augmented Dickey-Fuller test. The reported p-values are MacKinnon's unit root p-values taken from the package urca.

Lag length selection is done automatically in the ADF regression with the specified information criterion. If one of the modified criteria of Ng and Perron (2001) is used, the correction of Perron and Qu (2008) is applied. For very short time series (fewer than 50 time points) the maximum lag length is adjusted downward to avoid potential multicollinearity issues in the bootstrap. To overwrite data-driven lag length selection with a pre-specified lag length, simply set both the minimum 'min_lag' and maximum lag length 'max_lag' for the selection algorithm equal to the desired lag length.

Value

An object of class "bootUR", "htest" with the following components:

method

The name of the hypothesis test method;

data.name

The name of the variable on which the method is performed;

null.value

The value of the (gamma) parameter of the lagged dependent variable in the ADF regression under the null hypothesis. Under the null, the series has a unit root. Testing the null of a unit root then boils down to testing the significance of the gamma parameter;

alternative

A character string specifying the direction of the alternative hypothesis relative to the null value. The alternative postulates that the series is stationary;

estimate

The estimated value of the (gamma) parameter of the lagged dependent variable in the ADF regression;

statistic

The value of the test statistic of the ADF unit root test;

p.value

The p-value of the ADF unit root test.

Errors and warnings

Error: Multiple time series not allowed. Switch to a multivariate method such as boot_ur, or change argument data to a univariate time series.

The function provides a standard ADF test with asymptotic p-value. It does not support multiple time series

Examples

# standard ADF test on GDP_BE
GDP_BE_adf <- adf(MacroTS[, 1], deterministics = "trend")

[Package bootUR version 0.4.2 Index]