This paper addresses the problem of testing the hypothesis that an observed series is difference stationary. The alternative hypothesis is that the series is another nonstationary process; in particular, an autoregressive model with a random parameter is used. A locally best invariant test is developed assuming Gaussianity, and a representation of its asymptotic distribution as a mixture of Brownian motions is found. The performance of the test in finite samples is investigated by simulation. An example is given where the difference stationary assumption for a well-known data series is rejected.
Publié le : 1995-06-14
Classification:
Autoregression,
Brownian motion,
difference stationarity,
locally best invariant,
random coefficient,
weak convergence,
62M10,
62F03,
62F05
@article{1176324634,
author = {McCabe, B. P. M. and Tremayne, A. R.},
title = {Testing a Time Series for Difference Stationarity},
journal = {Ann. Statist.},
volume = {23},
number = {6},
year = {1995},
pages = { 1015-1028},
language = {en},
url = {http://dml.mathdoc.fr/item/1176324634}
}
McCabe, B. P. M.; Tremayne, A. R. Testing a Time Series for Difference Stationarity. Ann. Statist., Tome 23 (1995) no. 6, pp. 1015-1028. http://gdmltest.u-ga.fr/item/1176324634/