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Robust Estimation of GARMA Model Parameters with an Application to Cointegration among Interest Rates of Industrialized Countries

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  • Ramachandran, Rajalakshmi
  • Beaumont, Paul

Abstract

We review the literature on long memory ARFIMA and GARMA models and introduce a new efficient estimator for GARMA models, which we show to be robust. Next we conduct a Monte Carlo study to demonstrate the power of the Dickie-Fuller test when the data are generated from a stationary GARMA process. We conclude with a brief discussion of cointegration in the context of GARMA models with an application to international interest rates. Copyright 2001 by Kluwer Academic Publishers

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  • Ramachandran, Rajalakshmi & Beaumont, Paul, 2001. "Robust Estimation of GARMA Model Parameters with an Application to Cointegration among Interest Rates of Industrialized Countries," Computational Economics, Springer;Society for Computational Economics, vol. 17(2-3), pages 179-201, June.
    • Handle: RePEc:kap:compec:v:17:y:2001:i:2-3:p:179-201
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    Cited by:

    1. Paul M. Beaumont & Aaron D. Smallwood, 2024. "Conditional sum of squares estimation of k-factor GARMA models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(3), pages 501-543, September.
    2. José Manuel Belbute & Alfredo Marvão Pereira, 2016. "Does final energy demand in Portugal exhibit long memory? A fractional integration analysis," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 15(2), pages 59-77, August.
    3. Asai Manabu & Peiris Shelton & McAleer Michael & Allen David E., 2020. "Cointegrated Dynamics for a Generalized Long Memory Process: Application to Interest Rates," Journal of Time Series Econometrics, De Gruyter, vol. 12(1), pages 1-18, January.
    4. Aaron D. Smallwood & Stefan C. Norrbin, 2006. "Generalized long memory processes, failure of cointegration tests and exchange rate dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 409-417, May.
    5. Dissanayake, G.S. & Peiris, M.S. & Proietti, T., 2016. "State space modeling of Gegenbauer processes with long memory," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 115-130.
    6. Beaumont, Paul & Smallwood, Aaron, 2019. "Inference for likelihood-based estimators of generalized long-memory processes," MPRA Paper 96313, University Library of Munich, Germany.
    7. Asai, M. & Peiris, S. & McAleer, M.J. & Allen, D.E., 2018. "Cointegrated Dynamics for A Generalized Long Memory Process," Econometric Institute Research Papers EI 2018-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Beaumont, Paul & Smallwood, Aaron, 2019. "Conditional Sum of Squares Estimation of Multiple Frequency Long Memory Models," MPRA Paper 96314, University Library of Munich, Germany.

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