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Persistence and Nonstationary Models

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Author Info
B.P.M. McCabe
G.M. Martin ()
A.R. Tremayne

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Abstract

The aim of this paper is to examine the measurement of persistence in a range of time series models nested in the framework of Cramer (1961). This framework is a generalization of the Wold (1938) decomposition for stationary time series which, in addition to accommodating the standard I(0) and I(1) models, caters for alternative nonstationary processes. Three measures of persistence are considered, namely the long-run impulse response, variance ratio and autocorrelation functions. Particular emphasis is given to the behaviour of these measures in a range of nonstationary models. We document conflict that arises between different measures, applied to the same model, as well as conflict arising from the use of a given measure in different models. Precisely which persistence measures are time dependent and which are not, is highlighted. The nature of the general representation used also helps clarify what shock the impulse response function refers to in the case of models where more than one random disturbance impinges on the time series.

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Publisher Info
Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 16/03.

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Length: 25 pages
Date of creation: Sep 2003
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Handle: RePEc:msh:ebswps:2003-16

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Related research
Keywords: Cramer Representation; Stochastic Unit Root Model; Stochastic Integration; Impulse Response; Variance Ratio; Autocorrelation Function; Long Memory.;

Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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References listed on IDEAS
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  1. Campbell, John Y & Mankiw, N Gregory, 1987. "Are Output Fluctuations Transitory?," The Quarterly Journal of Economics, MIT Press, vol. 102(4), pages 857-80, November. [Downloadable!] (restricted)
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  2. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July. [Downloadable!] (restricted)
  3. Granger, E.J. & Swanson, N.R., 1996. "An introduction to stochastic Unit Root Processes," Papers 4-96-3, Pennsylvania State - Department of Economics.
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  4. Leybourne, S J & McCabe, B P M & Tremayne, A R, 1996. "Can Economic Time Series Be Differenced to Stationarity?," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 435-46, October.
  5. Peter C.B. Phillips, 1999. "Descriptive Econometrics for Nonstationary Time Series with Empirical Illustrations," Cowles Foundation Discussion Papers 1219, Cowles Foundation, Yale University. [Downloadable!]
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  6. John Y. Campbell & N. Gregory Mankiw, 1989. "International Evidence on the Persistence of Economic Fluctuations," NBER Working Papers 2498, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  7. Cochrane, John H, 1988. "How Big Is the Random Walk in GNP?," Journal of Political Economy, University of Chicago Press, vol. 96(5), pages 893-920, October. [Downloadable!] (restricted)
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