An Information Theoretic Approach to Econometrics

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.

Business & Money

Judge, George G. G.

Economics

Cambridge University Press

See the best price to sell, buy, or rent books by Judge, George G. G.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Judge, George G. G.

See the best price to sell, buy, or rent books by Judge, George G. G.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

An Information Theoretic Approach to Econometrics