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Noncausality and Inflation Persistence

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  • Markku Lanne

Abstract

We use noncausal autoregressions to examine the persistence properties of quarterly U.S. consumer price inflation from 1970:1.2012:2. These nonlinear models capture the autocorrelation structure of the inflation series as accurately as their conventional causal counterparts, but they allow for persistence to depend on the size and sign of shocks to inflation as well as the inflation rate. Inflation persistence has decreased since the early 1980.s, after which persistence is also greater following small and negative shocks than large and positive ones. At high levels of inflation, shocks are absorbed more slowly before the early 1980.s and faster thereafter compared to low levels of inflation.

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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.417856.de/dp1286.pdf
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Bibliographic Info

Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 1286.

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Length: 27 p.
Date of creation: 2013
Date of revision:
Handle: RePEc:diw:diwwpp:dp1286

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  1. van Dijk, Dick & Hans Franses, Philip & Peter Boswijk, H., 2007. "Absorption of shocks in nonlinear autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4206-4226, May.
  2. Bob Nobay & Ivan Paya & David A. Peel, 2010. "Inflation Dynamics in the U.S.: Global but Not Local Mean Reversion," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 135-150, 02.
  3. Breid, F. Jay & Davis, Richard A. & Lh, Keh-Shin & Rosenblatt, Murray, 1991. "Maximum likelihood estimation for noncausal autoregressive processes," Journal of Multivariate Analysis, Elsevier, vol. 36(2), pages 175-198, February.
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  5. Peter Tillmann & Maik H. Wolters, 2012. "The changing dynamics of US inflation persistence: a quantile regression approach," MAGKS Papers on Economics 201206, Philipps-Universit├Ąt Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  6. Lanne, Markku & Luoto, Jani, 2012. "Has US inflation really become harder to forecast?," Economics Letters, Elsevier, vol. 115(3), pages 383-386.
  7. Athanasios Orphanides & David H. Small & Volker Wieland & David W. Wilcox, 1997. "A quantitative exploration of the opportunistic approach to disinflation," Finance and Economics Discussion Series 1997-36, Board of Governors of the Federal Reserve System (U.S.).
  8. Lof Matthijs, 2013. "Noncausality and asset pricing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(2), pages 211-220, April.
  9. Lanne Markku & Saikkonen Pentti, 2011. "Noncausal Autoregressions for Economic Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-32, October.
  10. Manmohan S. Kumar & Tatsuyoshi Okimoto, 2007. "Dynamics of Persistence in International Inflation Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(6), pages 1457-1479, 09.
  11. Karadi, Peter & Reiff, Adam, 2012. "Large shocks in menu cost models," Working Paper Series 1453, European Central Bank.
  12. Lanne, Markku & Luoto, Jani & Saikkonen, Pentti, 2010. "Optimal Forecasting of Noncausal Autoregressive Time Series," MPRA Paper 23648, University Library of Munich, Germany.
  13. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
  14. Lanne, Markku & Luoma, Arto & Luoto, Jani, 2009. "Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models," MPRA Paper 23646, University Library of Munich, Germany.
  15. Andrews, Beth & Davis, Richard A. & Jay Breidt, F., 2006. "Maximum likelihood estimation for all-pass time series models," Journal of Multivariate Analysis, Elsevier, vol. 97(7), pages 1638-1659, August.
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