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.
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