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Stochastic Equicontinuity for Unbounded Dependent Heterogeneous Arrays

Published online by Cambridge University Press:  11 February 2009

Bruce E. Hansen
Affiliation:
Boston College and University of Rochester

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.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1996

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References

REFERENCES

Andrews, D.W.K. (1988) Laws of large numbers for dependent non-identically distributed random variables. Econometric Theory 4, 458467.CrossRefGoogle Scholar
Andrews, D.W.K. (1991) An empirical process central limit theorem for dependent non-identically distributed random variables. Journal of Multivariate Analysis 38, 187203.CrossRefGoogle Scholar
Andrews, D.W.K. (1993) An introduction to econometric applications of empirical process theory for dependent random variables. Econometric Reviews 12, 183216.CrossRefGoogle Scholar
Andrews, D.W.K. & Ploberger, W. (1994) Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 62, 13831414.CrossRefGoogle Scholar
Andrews, D.W.K. & Pollard, D. (1994) An introduction to functional central limit theorems for dependent stochastic processes. International Statistical Review 62, 119132.CrossRefGoogle Scholar
Arcones, M.A. & Yu, B. (1994) Central limit theorems for empirical and U-processes of stationary mixing sequences. Journal of Theoretical Probability 7, 4771.CrossRefGoogle Scholar
de Jong, R.M. (1993) Stochastic Equicontinuity for Unbounded Mixing Processes. Working paper, Free University Amsterdam.Google Scholar
Doukhan, P., Massart, P., & Rio, E. (1996) Invariance principles for absolutely regular empirical processes. Annales de 1'lnstitut H. Poincare, forthcoming.Google Scholar
Gallant, A.R. & White, H. (1988) A Unified Theory of Estimation and Inference for Nonlinear Dynamic Models. New York: Basil Blackwell.Google Scholar
Hall, P. & Heyde, C.C. (1980) Martingale Limit Theory and Its Applications. New York: Academic Press.Google Scholar
Hansen, B.E. (1991) Strong laws for dependent heterogeneous processes. Econometric Theory 7, 213221. (Erratum, 1992, Econometric Theory 8, 421-422.)CrossRefGoogle Scholar
Hansen, B.E. (1993) Inference in Threshold Models. Working paper, Boston College.Google Scholar
Hansen, B.E. (19%) Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica, forthcoming.Google Scholar
Ibragimov, LA. (1962) Some limit theorems for stationary processes. Theory of Probability and Its Applications 7, 349382.CrossRefGoogle Scholar
Leventhal, S. (1988) A uniform CLT for uniformly bounded families of martingale differences. Journal of Theoretical Probability 2, 271287.CrossRefGoogle Scholar
Massart, P. (1988) Invariance Principles for Empirical Processes: The Weakly Dependent Case, ch. 1. Ph.D. dissertation, University of Paris.Google Scholar
McLeish, D.L. (1975) A maximal inequality and dependent strong laws. Annals of Probability 3, 829839.CrossRefGoogle Scholar
Philipp, W. (1982) Invariance principles for sums of mixing random elements and the multi-variate empirical process. Colloquia Mathematica Societatis Jdnos Bolyai 36, 843873.Google Scholar
Pisier, C. (1983) Some applications of the metric entropy condition to harmonic analysis. Banach Spaces, Harmonic Analysis, and Probability Theory. Lecture Notes in Mathematics 995, 123154. New York: Springer.CrossRefGoogle Scholar
Pollard, D. (1990) Empirical Processes: Theory and Applications. CBMS Conference Series in Probability and Statistics, vol. 2. Hayward, CA: Institute of Mathematical Statistics.CrossRefGoogle Scholar
Wellner, J. (1992) Empirical processes in action: A review. International Statistical Review 60, 247269.CrossRefGoogle Scholar