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Nelson And Plosser Revisited: Evidence From Fractional Arima Models

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Author Info
Guglielmo Maria Caporale ()
Luis A. Gil-Alana
Abstract

In this paper fractionally integrated ARIMA (ARFIMA) models are estimated using an extended version of Nelson and Plosser’s (1982) dataset. The analysis employs Sowell’s (1992) maximum likelihood procedure. Such a parametric approach requires the model to be correctly specified in order for the estimates to be consistent. A model-selection procedure based on diagnostic tests on the residuals, together with several likelihood criteria, is adopted to determine the correct specification for each series. The results suggest that all series, except unemployment and bond yields, are integrated of order greater than one. Thus, the standard approach of taking first differences may result in stationary series with long memory behaviour.

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Paper provided by Economics and Finance Section, School of Social Sciences, Brunel University in its series Public Policy Discussion Papers with number 04-16.

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Length: 29 pages
Date of creation: Oct 2004
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Handle: RePEc:bru:bruppp:04-16

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Postal: Brunel University, Uxbridge, Middlesex UB8 3PH, UK

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  2. 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. [Downloadable!] (restricted)
  3. Stock, James H., 1991. "Confidence intervals for the largest autoregressive root in U.S. macroeconomic time series," Journal of Monetary Economics, Elsevier, vol. 28(3), pages 435-459, December. [Downloadable!] (restricted)
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  4. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November. [Downloadable!] (restricted)
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  5. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March. [Downloadable!] (restricted)
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  6. Crato, Nuno & Rothman, Philip, 1994. "Fractional integration analysis of long-run behavior for US macroeconomic time series," Economics Letters, Elsevier, vol. 45(3), pages 287-291. [Downloadable!] (restricted)
  7. Gil-Alana, L. A. & Robinson, P. M., 1997. "Testing of unit root and other nonstationary hypotheses in macroeconomic time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 241-268, October. [Downloadable!] (restricted)
  8. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November. [Downloadable!] (restricted)
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  9. D Marinucci & Peter M Robinson, 1998. "Alternative Forms of Fractional Brownian Motion - (Now published in Journal of Statistical Planning and Inference, 80 (1999), pp.111-122.)," STICERD - Econometrics Paper Series /1998/354, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE. [Downloadable!]
  10. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May. [Downloadable!] (restricted)
  11. Zivot, Eric & Andrews, Donald W K, 1992. "Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(3), pages 251-70, July.
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  12. Smith, Jeremy & Taylor, Nick & Yadav, Sanjay, 1995. "Comparing the Bias and Misspecification in Arfima Models," The Warwick Economics Research Paper Series (TWERPS) 442, University of Warwick, Department of Economics.
  13. Stock, James H., 1994. "Deciding between I(1) and I(0)," Journal of Econometrics, Elsevier, vol. 63(1), pages 105-131, July. [Downloadable!] (restricted)
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  14. 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. [Downloadable!] (restricted)
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