
Introduction to the Mathematical and Statistical Foundations of Econometrics
Herman J. Bierens - Collection Themes in modern econometrics
Résumé
This book is intended for use in a rigorous introductory Ph.D.-level course in econometrics, or in a field course in econometric theory. It covers the measure - theoretical foundation of probability theory, the multivariate normal distribution with its application to classical linear regression analysis, various laws of large numbers, central limit theorems and related results for independent random variables as well as for stationary time series, with applications to asymptotic inference of M-estimators, and maximum likelihood theory.
Sommaire
- Probability and measure
- Borel measurability, integration, and mathematical expectations
- Conditional expectations
- Distributions and transformations
- The multivariate normal distribution and its application to statistical inference
- Modes of convergence
- Dependent laws of large numbers and central limit theorems
- Maximum likelihood theory
Caractéristiques techniques
PAPIER | |
Éditeur(s) | Cambridge University Press |
Auteur(s) | Herman J. Bierens |
Collection | Themes in modern econometrics |
Parution | 17/03/2005 |
Nb. de pages | 340 |
Format | 15 x 23 |
Couverture | Broché |
Poids | 485g |
Intérieur | Noir et Blanc |
EAN13 | 9780521542241 |
ISBN13 | 978-0-521-54224-1 |
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