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A more general central limit theorem for m-dependent random variables with unbounded m


  • Romano, Joseph P.
  • Wolf, Michael


In this article, a general central limit theorem for a triangular array of m-dependent random variables is presented. Here, m may tend to infinity with the row index at a certain rate. Our theorem is a generalization of previous results. Some examples are given that show that the generalization is useful. In particular, we consider the limiting behavior of the sample mean of a combined sample of independent long-memory sequences, the limiting behavior of a spectral estimator, and the moving blocks bootstrap distribution. The examples make it clear the consideration of asymptotic behavior with the amount of dependence m increasing with n is useful even when the underlying processes are weakly dependent (or even independent), because certain natural statistics that arise in the analysis of time series have this structure. In addition, we provide an example to demonstrate the sharpness of our result.

Suggested Citation

  • Romano, Joseph P. & Wolf, Michael, 2000. "A more general central limit theorem for m-dependent random variables with unbounded m," Statistics & Probability Letters, Elsevier, vol. 47(2), pages 115-124, April.
  • Handle: RePEc:eee:stapro:v:47:y:2000:i:2:p:115-124

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    Cited by:

    1. Le Breton, Michel & Lepelley, Dominique & Smaoui, Hatem, 2012. "The Probability of Casting a Decisive Vote: From IC to IAC trhough Ehrhart's Polynomials and Strong Mixing," IDEI Working Papers 722, Institut d'Économie Industrielle (IDEI), Toulouse.
    2. Bücher, Axel & Kojadinovic, Ivan & Rohmer, Tom & Segers, Johan, 2014. "Detecting changes in cross-sectional dependence in multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 111-128.
    3. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2017. "Bootstrap and Asymptotic Inference with Multiway Clustering," Working Papers 1386, Queen's University, Department of Economics.
    4. Jacek Leśkow & Rafał Synowiecki, 2010. "On bootstrapping periodic random arrays with increasing period," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 71(3), pages 253-279, May.
    5. Harvey, Danielle J. & Weng, Qian & Beckett, Laurel A., 2010. "On an asymptotic distribution of dependent random variables on a 3-dimensional lattice," Statistics & Probability Letters, Elsevier, vol. 80(11-12), pages 1015-1021, June.
    6. Zhao, Zhibiao, 2010. "Density estimation for nonlinear parametric models with conditional heteroscedasticity," Journal of Econometrics, Elsevier, vol. 155(1), pages 71-82, March.
    7. Ayyala, Deepak Nag & Park, Junyong & Roy, Anindya, 2017. "Mean vector testing for high-dimensional dependent observations," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 136-155.
    8. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Marco Meyer & Jens-Peter Kreiss, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 377-397, May.
    9. Zhao, Zhibiao & Wu, Wei Biao, 2007. "Asymptotic theory for curve-crossing analysis," Stochastic Processes and their Applications, Elsevier, vol. 117(7), pages 862-877, July.
    10. Jiti Gao & Guangming Pan & Yanrong Yang, 2016. "CEstimation of Structural Breaks in Large Panels with Cross-Sectional Dependence," Monash Econometrics and Business Statistics Working Papers 12/16, Monash University, Department of Econometrics and Business Statistics.
    11. Geenens, Gery & Simar, Léopold, 2010. "Nonparametric tests for conditional independence in two-way contingency tables," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 765-788, April.
    12. Last, Michael & Shumway, Robert, 2008. "Detecting abrupt changes in a piecewise locally stationary time series," Journal of Multivariate Analysis, Elsevier, vol. 99(2), pages 191-214, February.
    13. Hui, Francis K.C. & Geenens, Gery, 2012. "Nonparametric bootstrap tests of conditional independence in two-way contingency tables," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 130-144.


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