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Stochastic Equicontinuity for Unbounded Dependent Heterogeneous Arrays

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  • Hansen, Bruce E.

Abstract

This paper establishes stochastic equicontinuity for classes of mixingales. Attention is restricted to Lipschitz-continuous parametric functions. Unlike some other empirical process theory for dependent data, our results do not require bounded functions, stationary processes, or restrictive dependence conditions. Applications are given to martingale difference arrays, strong mixing arrays, and near epoch dependent arrays.

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Bibliographic Info

Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 12 (1996)
Issue (Month): 02 (June)
Pages: 347-359

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Handle: RePEc:cup:etheor:v:12:y:1996:i:02:p:347-359_00

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References

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  1. Donald W.K. Andrews, 1989. "An Empirical Process Central Limit Theorem for Dependent Non-Identically Distributed Random Variables," Cowles Foundation Discussion Papers 907, Cowles Foundation for Research in Economics, Yale University.
  2. Donald W.K. Andrews, 1992. "An Introduction to Econometric Applications of Functional Limit Theory for Dependent Random Variables," Cowles Foundation Discussion Papers 1020, Cowles Foundation for Research in Economics, Yale University.
  3. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-30, March.
  4. Andrews, Donald W.K., 1988. "Laws of Large Numbers for Dependent Non-Identically Distributed Random Variables," Econometric Theory, Cambridge University Press, vol. 4(03), pages 458-467, December.
  5. Donald W.K. Andrews & Werner Ploberger, 1992. "Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative," Cowles Foundation Discussion Papers 1015, Cowles Foundation for Research in Economics, Yale University.
  6. Bierens, H.J., 1989. "A consistent conditional moment test of functional form," Serie Research Memoranda 0064, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
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Cited by:
  1. Gordon C.R. Kemp & J.M.C. Santos Silva, 2010. "Regression towards the mode," Economics Discussion Papers 686, University of Essex, Department of Economics.
  2. Valentina Corradi & Andres Fernandez & Norman Swanson, 2008. "Information in the revision process of real-time datasets," Working Papers 08-27, Federal Reserve Bank of Philadelphia.
  3. Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Whang, 2002. "Consistent Testing for Stochastic Dominance: A Subsampling Approach," FMG Discussion Papers dp407, Financial Markets Group.
  4. James H. Stock & Jonathan Wright, 1996. "Asymptotics for GMM Estimators with Weak Instruments," NBER Technical Working Papers 0198, National Bureau of Economic Research, Inc.
  5. Donald W. K. Andrews & Xu Cheng, 2012. "Estimation and Inference With Weak, Semiā€Strong, and Strong Identification," Econometrica, Econometric Society, vol. 80(5), pages 2153-2211, 09.
  6. Donald W. K. Andrews & Xu Cheng, 2011. "Maximum Likelihood Estimation and Uniform Inference with Sporadic Identification Failure," Cowles Foundation Discussion Papers 1824, Cowles Foundation for Research in Economics, Yale University.
  7. Katsumi Shimotsu & Peter C.B. Phillips, 2002. "Exact Local Whittle Estimation of Fractional Integration," Economics Discussion Papers 535, University of Essex, Department of Economics.
  8. Liangjun Su & Zhenlin Yang, 2007. "Instrumental Variable Quantile Estimation of Spatial Autoregressive Models," Development Economics Working Papers 22476, East Asian Bureau of Economic Research.

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