"This monograph is aimed at developing Doukhan/Louhichi's (1999) idea to measure asymptotic independence of a random process. The authors propose various examples of models fitting such conditions such as stable Markov chains, dynamical systems or more complicated models, nonlinear, non-Markovian, and heteroskedastic models with infinite memory. Most of the commonly used stationary models fit their conditions. The simplicity of the conditions is also their strength."--BOOK JACKET.
Series Statement
Lecture notes in statistics ; 190
Uniform Title
Lecture notes in statistics (Springer-Verlag) v. 190.
Includes bibliographical references (p. 305-315) and index.
Processing Action (note)
committed to retain
Contents
Preface -- Introduction -- Weak dependence -- Models -- Tools for non causal cases -- Tools for causal cases -- Applications of SLLN -- Central limit theorem -- Donsker principles -- Law of the iterated logarithm (LIL) -- The empirical process -- Functional estimation -- Spectral estimation -- Econometric applications and resampling.