Research Catalog

Title

Stochastic limit theory : an introduction for econometricians / James Davidson.

Author

Davidson, J. (James)

Publication

Oxford ; New York : Oxford University Press, 1994.

Items in the Library & Off-site

Holdings

Details

Description

xxii, 539 p. : ill.; 25 cm.

Summary

Econometricians, while using mathematical theory such as probability and limit theory at a demanding level, often do not have the advantage of a strong mathematical training. Using maths texts requires econometricians to ignore much material, and decipher unfamiliar notation, before reaching results useful and comprehensible to them. James Davidson has succeeded in clearly and rigorously explaining this mathematics to the econometricians who are increasingly using it. This book will serve as a technically self-contained handbook for advanced graduate students of econometrics, doctoral students, and academic or business econometricians who wish to improve their command of the mathematical processes they use. A wide-ranging coverage of mathematics combines the latest work with a lucid exposition of basic theories. The text covers statistical methods including probability theory, stochastic processes and their dependence structure, central limit theorems, and asymptotic distribution theory. It provides results directly relevant to econometricians, and indicates further reading. Davidson has included new material and results in central limit theorems from his research. Thus the book will appeal both as a survey and a research monograph.

Series Statement

Advanced texts in econometrics

Uniform Title

Advanced texts in econometrics

Subject

• Econometrics
• Limit theorems (Probability theory)
• Stochastic Processes
• Stochastic processes

Bibliography (note)

• Includes bibliographical references (p. 519-526) and index.

Processing Action (note)

• committed to retain

Contents

Pt. I. Mathematics. 1. Sets and numbers. 2. Limits and continuity. 3. Measure. 4. Integration. 5. Metric spaces. 6. Topology -- pt. II. Probability. 7. Probability spaces. 8. Random variables. 9. Expectations. 10. Conditioning. 11. Characteristic functions -- pt. III. Theory of stochastic processes. 12. Stochastic processes. 13. Dependence. 14. Mixing. 15. Martingales. 16. Mixingales. 17. Near-epoch dependence -- pt. IV. The law of large numbers. 18. Stochastic convergence. 19. Convergence in L[subscript p]-Norm. 20. The strong law of large numbers. 21. Uniform stochastic convergence -- pt. V. The central limit theorm. 22. Weak convergence of distributions. 23. The classical central limit theorem. 24. CLTs for dependent processes. 25. Some extensions -- pt. VI. The functional central limit theorem. 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.

ISBN

• 0198774028
• 0198774036 (pbk.)

LCCN

^^^95116362^//r95

OCLC

31267394

Owning Institutions

Harvard Library