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 II - Formulation and Solution of Stochastic Inverse Problems
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
Formulation and Solution of Stochastic Inverse Problems
In Part I, we reviewed the standard econometric enterprise where
the origin of observed data is characterized within a sufficiently constrained stochastic model formulation,
assumptions are used to define the underlying data sampling process (DSP), and
the model contains a sufficiently small number of unknowns so that information about them can be recovered-estimated by long-standing traditional econometric methods.
In this case, the econometric information recovery problem takes on the form of a well-posed just-determined or overdetermined problem where a solution exists, is unique, and there are a sufficient number of data observations to estimate the unknowns.
- Type
- Chapter
- Information
- An Information Theoretic Approach to Econometrics , pp. 67 - 68Publisher: Cambridge University PressPrint publication year: 2011