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

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Author Info

  • 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.

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File URL: http://hdl.handle.net/1765/1619
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Bibliographic Info

Paper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 9947/A.

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Date of creation: 08 Dec 1999
Date of revision:
Handle: RePEc:dgr:eureir:1765001619

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Web page: http://www.few.eur.nl/few

Related research

Keywords: bias correction; ARFIMA-GARCH; bootstrap test; higher order asymptotics; modified profile likelihood;

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Cited by:
  1. Taner Yigit, 2007. "Inflation Targeting : An Indirect Approach to Assess the Direct Impact," Departmental Working Papers 0706, Bilkent University, Department of Economics.
  2. Emma Iglesias & Garry Phillips, 2005. "Analysing one-month Euro-market interest rates by fractionally integrated models," Applied Financial Economics, Taylor and Francis Journals, vol. 15(2), pages 95-106.
  3. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Society for Computational Economics, vol. 38(4), pages 517-539, November.
  4. Isao Ishida & Toshiaki Watanabe, 2009. "Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model," CARF F-Series CARF-F-145, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  5. Vasco J. Gabriel & Luis F. Martins, 2000. "The Forecast Performance of Long Memory and Markov Switching Models," NIPE Working Papers 2/2000, NIPE - Universidade do Minho.
  6. Gadea, Maria & Mayoral, Laura, 2005. "The Persistence of Inflation in OECD Countries: A Fractionally Integrated Approach," MPRA Paper 815, University Library of Munich, Germany.
  7. E. Dubois & S. Lardic & V. Mignon, 2003. "The exact maximum likelihood-based test for fractional cointegration: critical values, power and size," THEMA Working Papers 2003-26, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  8. Taner Yigit, 2002. "Effects of Moments on Aggregation and Long Memory in Inflation," Departmental Working Papers 0210, Bilkent University, Department of Economics.
  9. Claudio Morana, 2000. "Measuring core inflation in the Euro area," Working Paper Series 36, European Central Bank.
  10. 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.
  11. Richard T. Baille & Claudio Morana, 2009. "Investigating Inflation Dynamics and Structural Change with an Adaptive ARFIMA Approach," ICER Working Papers - Applied Mathematics Series 06-2009, ICER - International Centre for Economic Research.
  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.
  13. Arielle Beyaert, 2004. "Fractional Output Convergence, with an Application to Nine Developed Countries," Econometric Society 2004 Australasian Meetings 280, Econometric Society.

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