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Effects of Moments on Aggregation and Long Memory in Inflation

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  • Taner Yigit

Abstract

There are two crucial conditions for cross-sectional aggregation of AR(1) parameters to produce long memory: 1) heterogeneity and 2) proximity to the unit root. We analyze role of moments, namely the mean and variance, of the distribution of the AR(1) coefficients in generating long memory. The positive relation between these moments and the order of integration suggests that the degree of fractional integration should decrease with a lower mean or variance. We investigate this result by first modeling long memory in inflation as a result of the aggregation of individual inflation expectations and then showing how the adoption of inflation targeting decreases the memory length in seven countries due to its moderating effect on individual inflation expectatio

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Bibliographic Info

Paper provided by Bilkent University, Department of Economics in its series Departmental Working Papers with number 0210.

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Date of creation: 2002
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Handle: RePEc:bil:bilpap:0210

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  1. Marcus J. Chambers, . "Long Memory and Aggregation in Macroeconomic Time Series," Economics Discussion Papers 437, University of Essex, Department of Economics.
  2. Torben G. Andersen & Tim Bollerslev, 1996. "Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns," NBER Working Papers 5752, National Bureau of Economic Research, Inc.
  3. 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.
  4. William R. Parke, 1999. "What Is Fractional Integration?," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 632-638, November.
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  6. Cheung, Yin-Wong & Diebold, Francis X., 1994. "On maximum likelihood estimation of the differencing parameter of fractionally-integrated noise with unknown mean," Journal of Econometrics, Elsevier, vol. 62(2), pages 301-316, June.
  7. Hassler, Uwe & Wolters, Jurgen, 1995. "Long Memory in Inflation Rates: International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 37-45, January.
  8. Sowell, Fallaw, 1992. "Modeling long-run behavior with the fractional ARIMA model," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 277-302, April.
  9. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
  10. Naish, Howard F., 1993. "The near optimality of adaptive expectations," Journal of Economic Behavior & Organization, Elsevier, vol. 20(1), pages 3-22, January.
  11. Lewbel, Arthur, 1994. "Aggregation and Simple Dynamics," American Economic Review, American Economic Association, vol. 84(4), pages 905-18, September.
  12. Christopher F. Baum & John Barkoulas & Mustafa Caglayan, 1996. "Persistence in International Inflation Rates," Boston College Working Papers in Economics 333., Boston College Department of Economics.
  13. Martin Evans & Paul Wachtel, 1993. "Inflation regimes and the sources of inflation uncertainty," Proceedings, Federal Reserve Bank of Cleveland, pages 475-520.
  14. Mankiw, N Gregory, 2001. "The Inexorable and Mysterious Tradeoff between Inflation and Unemployment," Economic Journal, Royal Economic Society, vol. 111(471), pages C45-61, May.
  15. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
  16. Figlewski, Stephen & Wachtel, Paul, 1981. "The Formation of Inflationary Expectations," The Review of Economics and Statistics, MIT Press, vol. 63(1), pages 1-10, February.
  17. Liu, Ming, 2000. "Modeling long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 99(1), pages 139-171, November.
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