Partial autocorrelation functions of the fractional ARIMA processes with negative degree of differencing.

DOI10.1016/S0047-259X(02)00027-1zbMath1036.62079OpenAlexW2033965389MaRDI QIDQ1427529

Yukio Kasahara, Akihiko Inoue

Publication date: 14 March 2004

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0047-259x(02)00027-1

zbMATH Keywords

Long memoryPrediction errorStationary processFractional ARIMA processPartial autocorrelation function

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Stationary stochastic processes (60G10)

Related Items (13)

BIAS CORRECTION OF SEMIPARAMETRIC LONG MEMORY PARAMETER ESTIMATORS VIA THE PREFILTERED SIEVE BOOTSTRAP ⋮ A Class of Antipersistent Processes ⋮ Representation theorems in finite prediction, with applications ⋮ An explicit representation of Verblunsky coefficients ⋮ Szegő's theorem and its probabilistic descendants ⋮ Multivariate prediction and matrix Szegő theory ⋮ Verblunsky coefficients and Nehari sequences ⋮ Simple matrix representations of the orthogonal polynomials for a rational spectral density on the unit circle ⋮ AR and MA representation of partial autocorrelation functions, with applications ⋮ Explicit representation of finite predictor coefficients and its applications ⋮ Financial Markets with Memory II: Innovation Processes and Expected Utility Maximization ⋮ The intersection of past and future for multivariate stationary processes ⋮ Rigidity for matrix-valued Hardy functions

Cites Work

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• Estimating the differencing parameter via the partial autocorrelation function
• Asymptotics for prediction errors of stationary processes with reflection positivity
• Asymptotics for the partial autocorrelation function of a stationary process
• Asymptotic behavior for partial autocorrelation functions of fractional ARIMA processes.
• Fractional ARIMA with stable innovations
• Fractional differencing
• AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING

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