Efficient inference in multivariate fractionally integrated time series models

DOI10.1111/j.1368-423X.2004.00122.xzbMath1094.91054OpenAlexW2129249812MaRDI QIDQ3156187

Morten Ørregaard Nielsen

Publication date: 6 January 2005

Published in: The Econometrics Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1368-423x.2004.00122.x

zbMATH Keywords

LR testlocal alternativefinite sample performancemultivariate ARFIMA modelmultivariate fractional unit root test

Mathematics Subject Classification ID

Economic time series analysis (91B84) Statistical methods; economic indices and measures (91B82)

Related Items (5)

Fractional integration and impulse responses: a bivariate application to real output in the USA and four Scandinavian countries ⋮ A multivariate long-memory model with structural breaks ⋮ Estimating multiple breaks in mean sequentially with fractionally integrated errors ⋮ TECHNOLOGY SHOCKS AND HOURS WORKED: A FRACTIONAL INTEGRATION PERSPECTIVE ⋮ Approximate state space modelling of unobserved fractional components

Uses Software

• Ox
• ARFIMA

Cites Work

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• Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression
• Statistical analysis of cointegration vectors
• Alternative forms of fractional Brownian motion
• Testing the null of stationarity for multiple time series
• Weak convergence of multivariate fractional processes
• Inference on the cointegration rank in fractionally integrated processes.
• The quasi-likelihood approach to statistical inference on multiple time-series with long-range dependence
• CONSISTENCY OF THE AVERAGED CROSS‐PERIODOGRAM IN LONG MEMORY SERIES
• THE NONSTATIONARY FRACTIONAL UNIT ROOT
• ON ASYMPTOTIC INFERENCE IN COINTEGRATED TIME SERIES WITH FRACTIONALLY INTEGRATED ERRORS
• ASYMPTOTIC INFERENCE FOR NONSTATIONARY FRACTIONALLY INTEGRATED AUTOREGRESSIVE MOVING-AVERAGE MODELS
• EFFICIENT LIKELIHOOD INFERENCE IN NONSTATIONARY UNIVARIATE MODELS
• A Fractional Dickey-Fuller Test for Unit Roots

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