This two-volume work aims to present as completely as possible the methods of statistical inference with special reference to their economic applications. It is a well-integrated textbook presenting a wide diversity of models in a coherent and unified framework. The reader will find a description not only of the classical concepts and results of mathematical statistics, but also of concepts and methods recently developed for the specific needs of econometrics. Although the two volumes do not demand a high level of mathematical knowledge, they do draw on linear algebra and probability theory. The breadth of approaches and the extensive coverage of this two-volume work provide for a thorough and entirely self-contained course in modern economics. Volume 1 provides an introduction to general concepts and methods in statistics and econometrics, and goes on to cover estimation and prediction. Volume 2 focuses on testing, confidence regions, model selection, and asymptotic theory.
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Book Description
This two-volume work aims to present as completely as possible the methods of statistical inference with specific reference to their economic applications. Volume 1 provides an introduction to general concepts and methods in statistics and econometrics, and goes on to cover estimation and prediction. Volume 2 focuses on testing, confidence regions, model selection, and asymptotic theory.
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7 of 7 people found the following review helpful:
5.0 out of 5 stars
Theoretically rigorous and useful,
This review is from: Statistics and Econometric Models: Volume 1, General Concepts, Estimation, Prediction and Algorithms (Themes in Modern Econometrics) (Paperback)
Gourieroux & Monfort's (translated) 2-volume set has beautiful theory but at the same time lends itself to applications. The theory is intuitive, rigorous and fun to read. It also makes the connection between the theory and application understandable and meaningful (something that other books, e.g., Casella & Berger, fail to do). Highly recommended.
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Inside This Book
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First Sentence:
The problem of modelling arises as soon as one wants to describe and analyze a real phenomenon. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
asymptotic least squares, nonrandomized estimator, pseudo maximum likelihood estimator, nonrandomized decision rules, latent model, bijective relationship, generalized nonlinear least squares, corresponding posterior distribution, partial sufficiency, asymptotic variance covariance matrices, posterior marginal distribution, constrained estimator, simulated residuals, covariance filter, generalized residuals, asymptotic variance covariance matrix, conditional median, factorization criterion, coherency condition, conditional model, best unbiased estimator, score algorithm, observable model, instrumental variable estimator, auxiliary parameters Key Phrases - Capitalized Phrases (CAPs): (learn more)
Strong Law of Large Numbers, Mathematical Statistics, Journal of Econometrics, Central Limit Theorem, Journal of the American Statistical Association, Monte Carlo, Academic Press, Cambridge University Press, Examples Example, Principles of Econometrics, Statistical Inference, Bayesian Inference, Consider Example, Cours de Statistique, Journal of the Royal Statistical Society, Notions Fondamentales de Statistique, References Amemiya, Theoretical Statistics, Theory of Point Estimation New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The problem of modelling arises as soon as one wants to describe and analyze a real phenomenon. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
asymptotic least squares, nonrandomized estimator, pseudo maximum likelihood estimator, nonrandomized decision rules, latent model, bijective relationship, generalized nonlinear least squares, corresponding posterior distribution, partial sufficiency, asymptotic variance covariance matrices, posterior marginal distribution, constrained estimator, simulated residuals, covariance filter, generalized residuals, asymptotic variance covariance matrix, conditional median, factorization criterion, coherency condition, conditional model, best unbiased estimator, score algorithm, observable model, instrumental variable estimator, auxiliary parameters Key Phrases - Capitalized Phrases (CAPs): (learn more)
Strong Law of Large Numbers, Mathematical Statistics, Journal of Econometrics, Central Limit Theorem, Journal of the American Statistical Association, Monte Carlo, Academic Press, Cambridge University Press, Examples Example, Principles of Econometrics, Statistical Inference, Bayesian Inference, Consider Example, Cours de Statistique, Journal of the Royal Statistical Society, Notions Fondamentales de Statistique, References Amemiya, Theoretical Statistics, Theory of Point Estimation New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 25
books:
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31
books
cite this book:
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- Principles of Econometrics by Henri Theil on 4 pages
- Theory of Point Estimation (Springer Texts in Statistics) by E.L. Lehmann on page 64, page 98, and page 156
- Theoretical Statistics by D.R. Cox on page 126, page 207, and page 441
- Statistical Inference (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by S.D. Silvey on page 65, page 157, and page 207
- Bayesian Econometrics by ZELLNER on page 110, and page 442
See all 25 books this book cites
- Econometrics of Qualitative Dependent Variables (Themes in Modern Econometrics) by Christian Gourieroux in Front Matter (1), and Front Matter (2)
- Econometric Methods with Applications in Business and Economics by Christiaan Heij on page 178, and page 266
- ARCH Models and Financial Applications (Springer Series in Statistics) by Christian Gourieroux in Back Matter (1), and Back Matter (2)
- An Introduction to Classical Econometric Theory by Paul A. Ruud in Back Matter
- Conditional Moment Estimation of Nonlinear Equation Systems: With an Application to an Oligopoly Model of Cooperative R&D (Lecture Notes in Economics and Mathematical Systems) by Joachim Inkmann in Back Matter
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