An Information Theoretic Approach to Econometrics
Cambridge University Press, 12/12/2011
EAN 9780521869591, ISBN10: 0521869595
Hardcover, 180 pages, 23.5 x 15.5 x 1.8 cm
Language: English
This book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods. Because most data are observational, practitioners work with indirect noisy observations and ill-posed econometric models in the form of stochastic inverse problems. Consequently, traditional econometric methods in many cases are not applicable for answering many of the quantitative questions that analysts wish to ask. After initial chapters deal with parametric and semiparametric linear probability models, the focus turns to solving nonparametric stochastic inverse problems. In succeeding chapters, a family of power divergence measure-likelihood functions are introduced for a range of traditional and nontraditional econometric-model problems. Finally, within either an empirical maximum likelihood or loss context, Ron C. Mittelhammer and George G. Judge suggest a basis for choosing a member of the divergence family.
Preface
1. Econometric information recovery
Part I. Traditional Parametric and Semiparametric Probability Models
Estimation and Inference
2. Formulation and analysis of parametric and semiparametric linear models
3. Method of moments, GMM, and estimating equations
Part II. Formulation and Solution of Stochastic Inverse Problems
4. A stochastic-empirical likelihood inverse problem
formulation and estimation
5. A stochastic-empirical likelihood inverse problem
inference
6. Kullback-Leibler information and the maximum empirical exponential likelihood
Part III. A Family of Minimum Discrepancy Estimators
7. The Cressie-Read family of divergence measures and likelihood functions
8. Cressie-Read-MEL-type estimators in practice
evidence of estimation and inference sampling performance
Part IV. Binary Discrete Choice MPD-EML Econometric Models
9. Family of distribution functions for the binary response-choice model
10. Estimation and inference for the binary response model based on the MPD family of distributions
Part V. Optimal Convex Divergence
11. Choosing the optimal divergence under quadratic loss
12. Epilogue.