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Dependence and stochastic limit theory (in Russian)

Author

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  • Jonathan Hill

    (University of North Carolina, Chapel Hill, USA)

Abstract

In this essay we provide the basic asymptotic theory that serves as background theory for estimators in time series. We outline concepts of dependence used for stochastic limit theory, covering mixing, mixingale and near epoch dependence properties. We then detail some of the most general probability and distribution limit theorems for these processes popularly employed for time series theory and applications.

Suggested Citation

  • Jonathan Hill, 2012. "Dependence and stochastic limit theory (in Russian)," Quantile, Quantile, issue 10, pages 1-31, December.
    • Handle: RePEc:qnt:quantl:y:2012:i:10:p:1-31
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    References listed on IDEAS

    as
    1. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
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    5. de Jong, Robert M., 1997. "Central Limit Theorems for Dependent Heterogeneous Random Variables," Econometric Theory, Cambridge University Press, vol. 13(3), pages 353-367, June.
    6. Eckhard Liebscher, 2005. "Towards a Unified Approach for Proving Geometric Ergodicity and Mixing Properties of Nonlinear Autoregressive Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(5), pages 669-689, September.
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