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Open AccessJournal ArticleDOI

Linear Serial Rank Tests for Randomness Against Arma Alternatives

Marc Hallin, +2 more
- 01 Sep 1985 - 
- Vol. 13, Iss: 3, pp 1156-1181
TLDR
In this article, a class of linear serial rank statistics for the problem of testing white noise against alternatives of ARMA serial dependence is introduced, and the efficiency properties of the proposed statistics are investigated, and an explicit formulation of the asymptotically most efficient score-generating functions is provided.
Abstract
In this paper we introduce a class of linear serial rank statistics for the problem of testing white noise against alternatives of ARMA serial dependence. The asymptotic normality of the proposed statistics is established, both under the null as well as alternative hypotheses, using LeCam's notion of contiguity. The efficiency properties of the proposed statistics are investigated, and an explicit formulation of the asymptotically most efficient score-generating functions is provided. Finally, we study the asymptotic relative efficiency of the proposed procedures with respect to their normal theory counterparts based on sample autocorrelations.

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MonographDOI

Modelling nonlinear economic time series

TL;DR: In this article, the authors propose a non-parametric approach for estimating parametric models from state space models and nonlinear and non-stationary models, based on nonparametric models and parametric linearity tests.
Journal ArticleDOI

Tests of Independence and Randomness Based on the Empirical Copula Process

TL;DR: In this article, it was shown that linear rank statistics have the same asymptotic distribution in both serial and non-serial copula processes, and that the limiting process has the same joint distribution in the serial case as in the non serial case.
Journal ArticleDOI

Kendall's tau for serial dependence

TL;DR: In this paper, the authors show how Kendall's tau can be adapted to test against serial dependence in a univariate time series context and provide formulas for the mean and variance of circular and noncircular versions of this statistic, and prove its asymptotic normality under the hypothesis of independence.
Journal ArticleDOI

Optimal rank-based procedures for time series analysis: testing an arma model against other arma models

Marc Hallin, +1 more
- 01 Mar 1988 - 
TL;DR: In this paper, a distribution-free asymptotically maximin-optimal test statistic was derived for a rank-based, weighted version of the classical Box-Pierce portmanteau statistic.
Journal ArticleDOI

Measures of Dependence and Tests of Independence

TL;DR: In this paper, the authors surveyed measures of dependence and resulting tests of independence and examined measures arising both from linear and nonlinear modeling, and the main emphasis is on some recently developed nonparametric tests using estimated distribution and density functions.