Stochastic Limit Theory: An Introduction for Econometricians

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Overview

This major new econometrics text surveys recent developments in the rapidly expanding field of asymptotic distribution theory, with a special emphasis on the problems of time dependence and heterogeneity. Designed for econometricians and advanced students with limited mathematical training, the book clearly lays out the necessary math and probability theory and uses numerous examples to make its data useful and comprehensible. It also includes original new material from Davidson's own research on central limit theorems.

About the Series
Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.

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Editorial Reviews

From the Publisher
"An excellent text."—W.G. Park, Assistant Professor of Economics, American University

"The chapters on the law of large numbers are superb. This book lays the foundation for serious econometrics. I wish I had spent a year studying it before graduate school. Well done!"—Henry Thompson, Auburn University

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Product Details

  • ISBN-13: 9780198774037
  • Publisher: Oxford University Press
  • Publication date: 12/8/1994
  • Series: Advanced Texts in Econometrics Series
  • Edition description: New Edition
  • Pages: 568
  • Product dimensions: 9.19 (w) x 6.13 (h) x 1.33 (d)

Meet the Author

University of Wales, Aberystwyth
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Table of Contents

Preface
Mathematical Symbols and Abbreviations
1 Sets and Numbers
2 Limits and Continuity
3 Measure
4 Integration
5 Metric Spaces
6 Topology
7 Probability Spaces
8 Random Variables
9 Expectations
10 Conditioning
11 Characteristic Functions
12 Stochastic Processes
13 Dependence
14 Mixing
15 Martingales
16 Mixingales
17 Near-Epoch Dependence
18 Stochastic Convergence
19 Convergence in L[subscript p]-Norm
20 The Strong Law of Large Numbers
21 Uniform Stochastic Convergence
22 Weak Convergence of Distributions
23 The Classical Central Limit Theorem
24 CLTs for Dependent Processes
25 Some Extensions
26 Weak Convergence in Metric Spaces
27 Weak Convergence in a Function Space
28 Cadlag Functions
29 FCLTs for Dependent Variables
30 Weak Convergence to Stochastic Integrals
Notes
References
Index
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