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 I - Traditional Parametric and Semiparametric Econometric Models: Estimation and Inference
Published online by Cambridge University Press: 05 June 2012
- 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
Traditional Parametric and Semiparametric Econometric Models: Estimation and Inference
In Part I, we use a familiar data sampling process to focus on parametric and semiparametric econometric models. This grouping nicely reflects the information, real or imagined, that the analyst uses in terms of the economic and data sampling process that is being modeled. In contrast to fully defined parametric models, semiparametric models cannot be fully defined in terms of the values of a finite number of parameters. In particular, there is no assertion made that a particular parametric family of probability distribution is known that fully defines the probability distribution underlying the data sampling process. Building on this base, in Part I we move in the direction of extremum formulations for analyzing models of this type. For a more complete discussion of the material and relevant proofs in Chapters 2 and 3, see Mittelhammer, Judge, and Miller (2000) and Mittelhammer (1996).
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- Publisher: Cambridge University PressPrint publication year: 2011