Estimating the differencing parameter via the partial autocorrelation function

DOI10.1016/S0304-4076(99)00076-7zbMath0968.62061OpenAlexW2156385490MaRDI QIDQ1586563

Terence Tai-Leung Chong

Publication date: 17 September 2001

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

Full work available at URL: https://doi.org/10.1016/s0304-4076(99)00076-7

zbMATH Keywords

fractionally integrated modeldifferencing parameter

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (6)

Partial autocorrelation functions of the fractional ARIMA processes with negative degree of differencing. ⋮ On the sample variance of explosive random coefficient autoregressive processes ⋮ Time series properties of aggregated AR(2) processes ⋮ Minimum distance estimation of stationary and non‐stationary ARFIMA processes ⋮ An Omnibus Test for Time Series ModelI(d) ⋮ The polynomial aggregated AR(1) model*

Cites Work

• Fractional integration analysis of long-run behavior for US macroeconomic time series
• A minimum distance estimator for long-memory processes
• Long memory processes and fractional integration in econometrics
• TESTS FOR FRACTIONAL INTEGRATION:A MONTE CARLO INVESTIGATION
• THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS
• Fractional differencing
• AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
• Long-Term Memory in Stock Market Prices
• ESTIMATION OF THE MEMORY PARAMETER FOR NONSTATIONARY OR NONINVERTIBLE FRACTIONALLY INTEGRATED PROCESSES

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