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How Flexible are the Inflation Targets? A Bayesian MCMC Estimator of the Long Memory Parameter in a State Space Model

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Author Info

  • Andersson, Fredrik N.G.

    ()
    (Department of Economics, Lund University)

  • Li, Yushu

    ()
    (Department of Business and Management Science, Norwegian School of Economics)

Abstract

Several central banks have adopted inflation targets. The implementation of these targets is flexible; the central banks aim to meet the target over the long term but allow inflation to deviate from the target in the short-term in order to avoid unnecessary volatility in the real economy. In this paper, we propose modeling the degree of flexibility using an AFRIMA model. Under the assumption that the central bankers control the long-run inflation rates, the fractional integration order captures the flexibility of the inflation targets. A higher integration order is associated with a more flexible target. Several estimators of the fractional integration order have been proposed in the literature. Grassi and Magistris (2011) show that a state-based maximum likelihood estimator is superior to other estimators, but our simulations show that their finding is over-biased for a nearly non-stationary time series. We resolve this issue by using a Bayesian Monte Carlo Markov Chain (MCMC) estimator. Applying this estimator to inflation from six inflation-targeting countries for the period 1999M1 to 2013M3, we find that inflation is integrated of order 0.8 to 0.9 depending on the country. The inflation targets are thus implemented with a high degree of flexibility.

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Bibliographic Info

Paper provided by Lund University, Department of Economics in its series Working Papers with number 2013:38.

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Length: 19 pages
Date of creation: 28 Nov 2013
Date of revision:
Handle: RePEc:hhs:lunewp:2013_038

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Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund,Sweden
Phone: +46 +46 222 0000
Fax: +46 +46 2224613
Web page: http://www.nek.lu.se/en
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Keywords: fractional integration; inflation-targeting; state space model;

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  1. KOOP , Gary & LEY , Eduardo & OSIEWALSKI , Jacek & STEEL , Mark, 1995. "Bayesian Analysis of Long Memory and Persistence using ARFIMA Models," CORE Discussion Papers 1995035, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
  3. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, September.
  4. Simeon Coleman and Kavita Sirichand, 2011. "Fractional integration and the volatility of UK interest rates," Working Papers 2011/02, Nottingham Trent University, Nottingham Business School, Economics Division.
  5. Mark J. Jensen, 2004. "Semiparametric Bayesian Inference of Long-Memory Stochastic Volatility Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 895-922, November.
  6. Stefano Grassi & Paolo Santucci de Magistris, 2011. "When Long Memory Meets the Kalman Filter: A Comparative Study," CREATES Research Papers 2011-14, School of Economics and Management, University of Aarhus.
  7. Shimotsu, Katsumi, 2010. "Exact Local Whittle Estimation Of Fractional Integration With Unknown Mean And Time Trend," Econometric Theory, Cambridge University Press, vol. 26(02), pages 501-540, April.
  8. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
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