Functions to estimate the number of differences required to make a given
time series stationary. ndiffs
estimates the number of first
differences necessary.
Arguments
- x
A univariate time series
- alpha
Level of the test, possible values range from 0.01 to 0.1.
- test
Type of unit root test to use
- type
Specification of the deterministic component in the regression
- max.d
Maximum number of non-seasonal differences allowed
- ...
Additional arguments to be passed on to the unit root test
Details
ndiffs
uses a unit root test to determine the number of differences
required for time series x
to be made stationary. If
test="kpss"
, the KPSS test is used with the null hypothesis that
x
has a stationary root against a unit-root alternative. Then the
test returns the least number of differences required to pass the test at
the level alpha
. If test="adf"
, the Augmented Dickey-Fuller
test is used and if test="pp"
the Phillips-Perron test is used. In
both of these cases, the null hypothesis is that x
has a unit root
against a stationary root alternative. Then the test returns the least
number of differences required to fail the test at the level alpha
.
References
Dickey DA and Fuller WA (1979), "Distribution of the Estimators for Autoregressive Time Series with a Unit Root", Journal of the American Statistical Association 74:427-431.
Kwiatkowski D, Phillips PCB, Schmidt P and Shin Y (1992) "Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root", Journal of Econometrics 54:159-178.
Osborn, D.R. (1990) "A survey of seasonality in UK macroeconomic variables", International Journal of Forecasting, 6:327-336.
Phillips, P.C.B. and Perron, P. (1988) "Testing for a unit root in time series regression", Biometrika, 72(2), 335-346.
Said E and Dickey DA (1984), "Testing for Unit Roots in Autoregressive Moving Average Models of Unknown Order", Biometrika 71:599-607.
See also
auto.arima
and ndiffs