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Investigating Inflation Dynamics and Structural Change with an Adaptive ARFIMA Approach

  • Richard T. Baille
  • Claudio Morana


Previous models of monthly CPI inflation time series have focused on possible regime shifts, non-linearities and the feature of long memory. This paper proposes a new time series model, named Adaptive ARFIMA; which appears well suited to describe inflation and potentially other economic time series data. The Adaptive ARFIMA model includes a time dependent intercept term which follows a Flexible Fourier Form. The model appears to be capable of succesfully dealing with various forms of breaks and discontinities in the conditional mean of a time series. Simulation evidence justifies estimation by approximate MLE and model specfication through robust inference based on QMLE. The Adaptive ARFIMA model when supplemented with conditional variance models is found to provide a good representation of the G7 monthly CPI inflation series.

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Paper provided by ICER - International Centre for Economic Research in its series ICER Working Papers - Applied Mathematics Series with number 06-2009.

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Length: 15 pages
Date of creation: May 2009
Date of revision:
Handle: RePEc:icr:wpmath:06-2009
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  1. Allan D. Brunner & Gregory D. Hess, 1990. "Are higher levels of inflation less predictable? A state-dependent conditional heteroskedasticity approach," Finance and Economics Discussion Series 141, Board of Governors of the Federal Reserve System (U.S.).
  2. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
  3. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
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  6. Hyung, Namwon & Franses, Philip Hans & Penm, Jack, 2006. "Structural breaks and long memory in US inflation rates: Do they matter for forecasting?," Research in International Business and Finance, Elsevier, vol. 20(1), pages 95-110, March.
  7. Charles S. Bos & Philip Hans Franses & Marius Ooms, 1998. "Long Memory and Level Shifts: Re-Analyzing Inflation Rates," Tinbergen Institute Discussion Papers 98-039/4, Tinbergen Institute.
  8. Bai, Jushan, 1997. "Estimating Multiple Breaks One at a Time," Econometric Theory, Cambridge University Press, vol. 13(03), pages 315-352, June.
  9. 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.
  10. Francis X. Diebold & Atsushi Inoue, 2000. "Long Memory and Regime Switching," NBER Technical Working Papers 0264, National Bureau of Economic Research, Inc.
  11. Claudio Morana & Fabio Cesare Bagliano, 2007. "Inflation and monetary dynamics in the USA: a quantity-theory approach," Applied Economics, Taylor & Francis Journals, vol. 39(2), pages 229-244.
  12. Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb..
  13. Baillie, Richard T. & Kapetanios, George, 2007. "Testing for Neglected Nonlinearity in Long-Memory Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 447-461, October.
  14. Junsoo Lee & Walter Enders, 2004. "Testing for a unit-root with a nonlinear Fourier function," Econometric Society 2004 Far Eastern Meetings 457, Econometric Society.
  15. Morana Claudio, 2002. "Common Persistent Factors in Inflation and Excess Nominal Money Growth and a New Measure of Core Inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(3), pages 1-40, November.
  16. Katsumi Shimotsu, 2006. "Simple (but effective) tests of long memory versus structural breaks," Working Papers 1101, Queen's University, Department of Economics.
  17. Christian Conrad & Berthold R. Haag, 2006. "Inequality Constraints in the Fractionally Integrated GARCH Model," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(3), pages 413-449.
  18. George Kapetanios, 2002. "Testing for Neglected Nonlinearity in Long Memory Models," Working Papers 473, Queen Mary University of London, School of Economics and Finance.
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