Biometrika
Опубликовано на портале: 13-04-2004
Peter Charles Bonest Phillips, Pierre Perron
Biometrika.
1988.
Vol. 75.
No. 2.
P. 335-346.
This paper proposes new tests for detecting the presence of a unit root in quite
general time series models. Our approach is nonparametric with respect to nuisance
parameters and thereby allows for a very wide class of weakly dependent and possibly
heterogeneously distributed data. The tests accommodate models with a fitted drift
and a time trend so that they may be used to discriminate between unit root nonstationarity
and stationarity about a deterministic trend. The limiting distributions of the statistics
are obtained under both the unit root null and a sequence of local alternatives.
The latter noncentral distribution theory yields local asymptotic power functions
for the tests and facilitates comparisons with alternative procedures due to Dickey
${\tt\&}$ Fuller. Simulations are reported on the performance of the new tests in
finite samples.
Опубликовано на портале: 05-01-2003
David A. Dickey, Elmahdy Said Said
Biometrika.
1984.
Vol. 71.
No. 3.
P. 599-607.
Recently, methods for detecting unit roots in autoregressive and autoregressive-moving
average time series have been proposed. The presence of a unit root indicates that
the time series is not stationary but that differencing will reduce it to stationarity.
The tests proposed to data require specification of the number of autoregressive
and moving average coefficients in the model. In this paper we develop a test for
unit roots which is based on an approximation of an autoregressive-moving average
model by an autoregression. The test statistic is standard output from most regression
programs and has a limit distribution whose percentiles have been tabulated. An example
is provided.
Опубликовано на портале: 31-03-2004
John Aitchison, Samuel D. Silvey
Biometrika.
1957.
Vol. 44.
No. 1/2.
P. 131-140.
The generalized probit analysis which we will discuss in this paper arose from a problem in entomology. We will suggest a solution to this particular problem and then consider a more general situation where this method of solution might be appropriate.
Опубликовано на портале: 07-04-2004
Greta M. Ljung, George E.P. Box
Biometrika.
1979.
Vol. 66.
No. 2.
P. 265-270.
This paper examines the likelihood function of a stationary autoregressive-moving
average model of order (p,q) and presents a method for its evaluation. Numerical
results illustrating the computational efficiency of the method are given.
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