How Flexible are the Inflation Targets? A Bayesian MCMC Estimator of the Long Memory Parameter in a State Space Model
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.
|Date of creation:||28 Nov 2013|
|Contact details of provider:|| 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
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Katsumi Shimotsu, 2006.
"Exact Local Whittle Estimation of Fractional Integration with Unknown Mean and Time Trend,"
1061, Queen's University, Department of Economics.
- 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.
- Simeon Coleman & Kavita Sirichand, 2011.
"Fractional integration and the volatility of UK interest rates,"
Discussion Papers in Economics
11/29, Department of Economics, University of Leicester, revised May 2011.
- Coleman, Simeon & Sirichand, Kavita, 2012. "Fractional integration and the volatility of UK interest rates," Economics Letters, Elsevier, vol. 116(3), pages 381-384.
- 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.
- 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.
- 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).
- Koop, Gary & Ley, Eduardo & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian analysis of long memory and persistence using ARFIMA models," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 149-169.
- Gary Koop, 1995. "Bayesian Analysis of Long Memory and Persistence using ARFIMA Models," Working Papers gkoop-95-01, University of Toronto, Department of Economics.
- Gary Koop & Eduardo Ley & Jacek Osiewalski & Mark F.J. Steel, 1995. "Bayesian Analysis of Long Memory and Persistence using ARFIMA Models," Econometrics 9505001, EconWPA, revised 11 Jul 1995.
- Koop, G. & Ley, E. & Osiewalski, J. & Steel, M. F. J., "undated". "Bayesian analysis of long memory and persistence using ARFIMA models," CORE Discussion Papers RP 1246, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Stefano Grassi & Paolo Santucci de Magistris, 2011.
"When Long Memory Meets the Kalman Filter: A Comparative Study,"
CREATES Research Papers
2011-14, Department of Economics and Business Economics, Aarhus University.
- Grassi, Stefano & Santucci de Magistris, Paolo, 2014. "When long memory meets the Kalman filter: A comparative study," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 301-319.
- Durbin, James & Koopman, Siem Jan, 2001.
"Time Series Analysis by State Space Methods,"
Oxford University Press, number 9780198523543, December.
- Tom Doan, "undated". "SEASONALDLM: RATS procedure to create the matrices for the seasonal component of a DLM," Statistical Software Components RTS00251, Boston College Department of Economics.
- 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.
- Fredrik Andersson, 2014. "Exchange rates dynamics revisited: a panel data test of the fractional integration order," Empirical Economics, Springer, vol. 47(2), pages 389-409, September.
- 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.
When requesting a correction, please mention this item's handle: RePEc:hhs:lunewp:2013_038. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (David Edgerton)
If references are entirely missing, you can add them using this form.