A Bootstrap Invariance Principle for Highly Nonstationary Long Memory Processes

Research, Preprint OPEN
George Kapetanios;
  • Publisher: London: Queen Mary University of London, Department of Economics
  • Subject: C22 | C15 | Bootstrap-Verfahren | Long memory, Bootstrap | Zeitreihenanalyse
    • jel: jel:C22 | jel:C15
      ddc: ddc:330

This paper presents an invariance principle for highly nonstationary long memory processes, defined as processes with long memory parameter lying in (1, 1.5). This principle provides the tools for showing asymptotic validity of the bootstrap in the context of such proce... View more
  • References (17)
    17 references, page 1 of 2

    Andrews, D. W. K., and O. Liebeman (2002): \Higher-order Improvements of the Parametric Bootstrap for Long-memory Gaussian Processes," Cowles Foundation Discussion Paper No. 1378.

    Chung, C. F., and R. T. Baillie (1993): \Small Sample Bias in Conditional Sum of Squares Estimators of Fractionally Integrated ARMA Models," Empirical Economics, 18, 791{806.

    Davidson, J., and R. DeJong (2000): \The Functional Central Limit Theorem and Convergence to Stochastic Integrals II: Fractionally Integrated Processes," Econometric Theory, 16, 621{642.

    Granger, C. W. J., and R. Joyeux (1980): \An Introduction to Long Memory Time Series Models and Fractional DiĀ®erencing," Journal of Time Series Analysis, 1, 15{39.

    Hamilton, J. D. (1994): Time Series Analysis. Princeton University Press.

    Hannan, E. J., and L. Kavalieris (1983): \The Convergence of Autocorrelations and Autoregressions," Australian Journal of Statistics, 25, 287{297.

    Hidalgo, J. (2003): \An alternative bootstrap to moving blocks for time series regression models," Journal of Econometrics, 117, 369{399.

    Inoue, A., and L. Kilian (2003): \The continuity of the Limit Distribution in the Parameter of Interest Is Not Essential for the Validity of the Bootstrap," Econometric Theory, 19, 944{961.

    Konstantopoulos, T., and A. Sakhanenko (2003): \Convergence and Convergence Rate to Fractional Brownian Motion for Weighted Random Sums," Mimeo, University of Texas at Austin.

    Ng, S., and P. Perron (1995): \Unit root tests in ARMA models with data-dependent methods for the selection of the truncation lag," Journal of the American Statistical Association, 90, 268{281.

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