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Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and UK inflation

Author

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  • Ooms, M.
  • Doornik, J.A.

Abstract

We discuss computational aspects of likelihood-based specification, estimation,inference, and forecasting of possibly nonstationary series with long memory. We use the \\ARFIMA$(p,d,q)$ model with deterministic regressors and we compare sampling characteristics of approximate and exact first-order asymptotic methods. We extend the analysis using a higher-order asymptotic method, suggested by \\cite{CoxRe.87}. Efficient computation and simulation allow us to apply parametric bootstrap inference as well. We investigate the relevance of the differences between the methods for the time-series analysis of monthly core consumer price inflation in the US and quarterly overall consumer price inflation in the UK. We concentrate on (stationarity) tests for the order of integration and on inference for out-of-sample forecasts of the price level.

Suggested Citation

  • Ooms, M. & Doornik, J.A., 1999. "Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and UK inflation," Econometric Institute Research Papers EI 9947/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1619
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    Citations

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    Cited by:

    1. Jussi Tolvi, 2003. "Long memory and outliers in stock market returns," Applied Financial Economics, Taylor & Francis Journals, vol. 13(7), pages 495-502.
    2. Bhansali, R. J. & Kokoszka, P. S., 2002. "Computation of the forecast coefficients for multistep prediction of long-range dependent time series," International Journal of Forecasting, Elsevier, vol. 18(2), pages 181-206.
    3. Arielle Beyaert, 2004. "Fractional Output Convergence, with an Application to Nine Developed Countries," Econometric Society 2004 Australasian Meetings 280, Econometric Society.
    4. Müller-Kademann Christian, 2015. "Internal Validation of Temporal Disaggregation: A Cloud Chamber Approach," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(3), pages 298-319, June.
    5. Isao Ishida & Toshiaki Watanabe, 2009. "Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model," CIRJE F-Series CIRJE-F-608, CIRJE, Faculty of Economics, University of Tokyo.
    6. María Dolores Gadea & Laura Mayoral, 2006. "The Persistence of Inflation in OECD Countries: A Fractionally Integrated Approach," International Journal of Central Banking, International Journal of Central Banking, vol. 2(1), March.
    7. Emmanuel Dubois & Sandrine Lardic & Valérie Mignon, 2004. "The Exact Maximum Likelihood-Based Test for Fractional Cointegration: Critical Values, Power and Size," Computational Economics, Springer;Society for Computational Economics, vol. 24(3), pages 239-255, July.
    8. Yigit, Taner M., 2010. "Inflation targeting: An indirect approach to assess the direct impact," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1357-1368, November.
    9. Doornik, Jurgen A. & Ooms, Marius, 2003. "Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 333-348, March.
    10. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 38(4), pages 517-539, November.
    11. Morana, Claudio, 2000. "Measuring core inflation in the euro area," Working Paper Series 36, European Central Bank.
    12. Doornik, Jurgen A. & Ooms, Marius, 2003. "Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 333-348, March.

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