Semiparametric Stationarity and Fractional Unit Roots Tests Based on Data-Driven Multidimensional Increment Ratio Statistics

Bardet Jean-Marc () and Dola Béchir ()
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Bardet Jean-Marc: SAMM, Université Panthéon-Sorbonne (Paris I), 90 rue de Tolbiac, 75013 Paris, France
Dola Béchir: SAMM, Université Panthéon-Sorbonne (Paris I), 90 rue de Tolbiac, 75013 Paris, France

Journal of Time Series Econometrics, 2016, vol. 8, issue 2, 115-153

Abstract: In this paper, we show that the central limit theorem (CLT) satisfied by the data-driven Multidimensional Increment Ratio (MIR) estimator of the memory parameter d established in Bardet and Dola (2012. Adaptive Estimator of the Memory Parameter and Goodness-of-Fit Test Using a Multidimensional Increment Ratio Statistic.” Journal of Multivariate Analysis 105:222–40) for dϵ(–0.5, 0.5) can be extended to a semiparametric class of Gaussian fractionally integrated processes with memory parameter dϵ(–0.5, 1.25). Since the asymptotic variance of this CLT can be estimated, by data-driven MIR tests for the two cases of stationarity and non-stationarity, so two tests are constructed distinguishing the hypothesis d

Keywords: Gaussian fractionally integrated processes; semiparametric estimators of the memory parameter; test of long-memory; stationarity test; fractional unit roots test (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1515/jtse-2014-0031

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