> Message: 1 > Date: Fri, 28 Apr 2017 11:07:40 +0000 > From: T.Riedle <tr...@kent.ac.uk> > To: "R-help@r-project.org" <R-help@r-project.org> > Subject: [R] Augmented Dickey Fuller test > Message-ID: <1493377701072.16...@kent.ac.uk> > Content-Type: text/plain; charset="UTF-8" > > Dear all, > > I am trying to run an ADF test using the adf.test() function in the tseries > package and the ur.df() function in the urca package. The results I get > contrast sharply. Whilst the adf.test() indicates stationarity which is in > line with the corresponding graph, the ur.df() indicates non-stationarity. >
In a simple example I can't reproduce your finding. The test statistic of adf.test() and ur.df() are identical: > library(urca) > library(tseries) > > set.seed(1) > > x <- rnorm(1000) # no unit-root > adf.test(x) Augmented Dickey-Fuller Test data: x Dickey-Fuller = -9.9291, Lag order = 9, p-value = 0.01 alternative hypothesis: stationary Warning message: In adf.test(x) : p-value smaller than printed p-value > ur.df(x, lags=trunc((length(x)-1)^(1/3)), type="trend") ############################################################### # Augmented Dickey-Fuller Test Unit Root / Cointegration Test # ############################################################### The value of the test statistic is: -9.9291 32.869 49.2953 > > y <- diffinv(x) # contains a unit-root > adf.test(y) Augmented Dickey-Fuller Test data: y Dickey-Fuller = -2.5115, Lag order = 9, p-value = 0.3618 alternative hypothesis: stationary > ur.df(y, lags=trunc((length(y)-1)^(1/3)), type="trend") ############################################################### # Augmented Dickey-Fuller Test Unit Root / Cointegration Test # ############################################################### The value of the test statistic is: -2.5115 2.4203 3.5281 > > > Why does this happen? Could anybody explain the adf.test() function in more > detail? How does adf.test() select the number of lags is it AIC or BIC and > how does it take an intercept and/or a trend into account? ?adf.test Details The general regression equation which incorporates a constant and a linear trend is used and the t-statistic for a first order autoregressive coefficient equals one is computed. The number of lags used in the regression is k. The default value of trunc((length(x)-1)^(1/3)) corresponds to the suggested upper bound on the rate at which the number of lags, k, should be made to grow with the sample size for the general ARMA(p,q) setup. References A. Banerjee, J. J. Dolado, J. W. Galbraith, and D. F. Hendry (1993): Cointegration, Error Correction, and the Econometric Analysis of Non-Stationary Data, Oxford University Press, Oxford. S. E. Said and D. A. Dickey (1984): Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order. Biometrika 71, 599–607. > > > > Help is greatly appreciated. > > > > Thanks in advance. > > [[alternative HTML version deleted]] > Best regards Adrian -- Adrian Trapletti Steinstrasse 9b, 8610 Uster, Switzerland P +41 44 994 56 30 | M +41 79 103 71 31 adr...@trapletti.org | www.trapletti.org ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.