ADF.test: Augmented Dickey-Fuller Test

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/adf.R

Description

This function computes the augmented Dickey-Fuller statistic for testing the null hypothesis that the long run unit root 1 exists.

Usage

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    ADF.test (wts, itsd, regvar=0, selectlags=list(mode="signf", Pmax=NULL))
  

Arguments

wts

a univariate time series object.

itsd

deterministic components to include in the model. Three types of regressors can be included: regular deterministic components, seasonal deterministic components, and any regressor variable previously defined by the user.

This argument must be a vector object with the following elements: c(0,0,c(0)), if the first and/or second elements are set equal to 1, it indicates that an intercept, and/or linear trend, respectively, are included. The third element is a vector indicating which seasonal dummies should be included. If no seasonal dummies are desired it must be set equal to zero. For example, regular=c(1,0,c(1,2,3)) would include an intercept, no trend, and the first three seasonal dummies.

regvar

regressor variables. If none regressor variables are considered, this object must be set equal to zero, otherwise, the names of a matrix object previously defined should be indicated.

selectlags

lag selection method. A list object indicating the method to select lags, mode, and the maximum lag considered. Available methods are "aic", "bic", and "signf". See details. Pmax is a numeric object indicating the maximum lag order. By default, the maximum number of lags considered is round(10*log10(n)), where n is the number of observations.

Details

The auxiliar regression is defined as,

δ y_t = ρ y_{t-1} + ε_t,

where δ is the first order operator. Hence, under the null hypothesis ρ=0 and the long run unit root 1 exists.

Available methods are the following. "aic" and "bic" follows a top-down strategy based on the Akaike's and Schwarz's information criteria, and "signf" removes the non-significant lags at the 10% level of significance until all the selected lags are significant. By default, the maximum number of lags considered is round(10*log10(n)), where n is the number of observations.

It is also possible to set the argument selectlags equals to a vector, mode=c(1,3,4), then those lags are directly included in the auxiliar regression and Pmax is ignored.

Value

An object of class adfstat-class.

Author(s)

Javier Lopez-de-Lacalle [email protected] and Ignacio Diaz-Emparanza [email protected]

References

D.A. Dickey and W.A. Fuller (1981), Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057-1071.

W.A. Fuller (1976), Introduction to Statistical Time Series. Jonh Wiley, New York.

See Also

ADF.rectest.

Examples

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    ## ADF test with constant, trend and seasonal dummies.
    data(AirPassengers)
    lairp <- log(AirPassengers)
    adf.out1 <- ADF.test(wts=lairp, itsd=c(1,1,c(1:11)),
                  regvar=0, selectlags=list(mode="bic", Pmax=12))
    adf.out1
    adf.out2 <- ADF.test(wts=lairp, itsd=c(1,1,c(1:11)),
                  regvar=0, selectlags=list(mode="signf", Pmax=NULL))
    adf.out2
  

uroot documentation built on May 31, 2017, 5:01 a.m.

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