Book contents
- Frontmatter
- Contents
- Preface
- One Econometric Information Recovery
- Part I Traditional Parametric and Semiparametric Econometric Models: Estimation and Inference
- Part II Formulation and Solution of Stochastic Inverse Problems
- Part III A Family of Minimum Discrepancy Estimators
- Part IV Binary–Discrete Choice Minimum Power Divergence (MPD) Measures
- Part V Optimal Convex Divergence
- Abbreviations
- Index
Part V - Optimal Convex Divergence
Published online by Cambridge University Press: 05 June 2012
Book contents
- Frontmatter
- Contents
- Preface
- One Econometric Information Recovery
- Part I Traditional Parametric and Semiparametric Econometric Models: Estimation and Inference
- Part II Formulation and Solution of Stochastic Inverse Problems
- Part III A Family of Minimum Discrepancy Estimators
- Part IV Binary–Discrete Choice Minimum Power Divergence (MPD) Measures
- Part V Optimal Convex Divergence
- Abbreviations
- Index
Summary
Optimal Convex Divergence
In the previous four chapters, we have introduced a flexible family of probability distributions, likelihood functions, estimators, and inference procedures based on information theoretic concepts. The usual case in econometrics is that the noisy indirect data are observed and known and the correct model underlying the genesis of that data is not. In this part of the book, to further address the unknown nature of the model, we propose taking a convex combination of two or more members of the estimators derived from members of the Cressie-Read family of divergence measures and optimize that combination relative to quadratic loss.
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- Information
- An Information Theoretic Approach to Econometrics , pp. 205 - 206Publisher: Cambridge University PressPrint publication year: 2011