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Fuller Test (fuller + test)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting60%

Selected Abstracts

FINITE SAMPLE EFFECTS OF PURE SEASONAL MEAN SHIFTS ON DICKEY,FULLER TESTS: A SIMULATION STUDY

THE MANCHESTER SCHOOL, Issue 5 2008
ARTUR C. B. DA SILVA LOPESArticle first published online: 18 AUG 200
In this paper, it is demonstrated by simulation that, contrary to a widely held belief, pure seasonal mean shifts,i.e. seasonal structural breaks which affect only the seasonal cycle,really do matter for Dickey,Fuller long-run unit root tests. Both size and power properties are affected by such breaks but using the t -sig method for lag selection induces a stabilizing effect. Although most results are reassuring when the t -sig method is used, some concern with this type of breaks cannot be disregarded. Further evidence on the poor performance of the t -sig method for quarterly time series in standard (no-break) cases is also presented. [source]

Unit-root testing: on the asymptotic equivalence of Dickey,Fuller with the log,log slope of a fitted autoregressive spectrum

JOURNAL OF TIME SERIES ANALYSIS, Issue 3 2010
Evangelos E. Ioannidis
In this article we consider the problem of testing for the presence of a unit root against autoregressive alternatives. In this context we prove the asymptotic equivalence of the well-known (augmented) Dickey,Fuller test with a test based on an appropriate parametric modification of the technique of log-periodogram regression. This modification consists of considering, close to the origin, the slope (in log,log coordinates) of an autoregressively fitted spectral density. This provides a new interpretation of the Dickey,Fuller test and closes the gap between it and log-periodogram regression. This equivalence is based on monotonicity arguments and holds on the null as well as on the alternative. Finally, a simulation study provides indications of the finite-sample behaviour of this asymptotic equivalence. [source]

A Direct Test for Cointegration Between a Pair of Time Series

JOURNAL OF TIME SERIES ANALYSIS, Issue 2 2002
STEPHEN J. LEYBOURNE
In this paper we introduce a new test of the null hypothesis of no cointegration between a pair of time series. For a very simple generating model, our test compares favourably with the Engle,Granger/Dickey,Fuller test and the Johansen trace test. Indeed, shortcomings of the former motivated the development of our test. The applicability of our test is extended to series generated by low-order vector autoregressions. Again, we find evidence that this general version of our test is more powerful than the Johansen test. The paper concludes with an empirical example in which the new test finds strong evidence of cointegration, but the Johansen test does not. [source]

Power of Tests for Unit Roots in the Presence of a Linear Trend,

OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 5 2008
Bent Nielsen
Abstract Dickey and Fuller [Econometrica (1981) Vol. 49, pp. 1057,1072] suggested unit-root tests for an autoregressive model with a linear trend conditional on an initial observation. TPower of tests for unit roots in the presence of a linear trendightly different model with a random initial value in which nuisance parameters can easily be eliminated by an invariant reduction of the model. We show that invariance arguments can also be used when comparing power within a conditional model. In the context of the conditional model, the Dickey,Fuller test is shown to be more stringent than a number of unit-root tests motivated by models with random initial value. The power of the Dickey,Fuller test can be improved by making assumptions to the initial value. The practitioner therefore has to trade-off robustness and power, as assumptions about initial values are hard to test, but can give more power. [source]

Behaviour of the standard and symmetric Dickey,Fuller-type tests when there is a break under the null hypothesis

THE ECONOMETRICS JOURNAL, Issue 1 2000
Stephen J. Leybourne
We examine the behaviour of tests of the unit root null hypothesis when the true generating model is I (1) with a break. Asymptotic distributional results are derived predicting that the standard Dickey,Fuller test will in that case yield frequent rejections of the null for relatively early breaks. This phenomenon is most severe for lowest values of the break fraction under a break in level, and for a break in slope occurring about 15% of the way through the series. However, asymptotics predict that the phenomenon will not be present for a modified test based on a symmetric weighted estimator. The theoretical predictions are confirmed by simulation evidence. [source]