Simulation-Based Inference in Econometrics: Methods and Applications
By: Mariano, Roberto
| Schuermann, Til | Weeks, Melvyn J.
Material type: 
BookPublisher: Cambridge et al., Cambridge University Press, 2000Description: 462 pages.ISBN: 0-521-59112-0.Subject(s): econometric model | econometrics | statistical inference | simulation-based inferenceSummary:
Description
Contents
Resources
Courses
About the Authors
This substantial volume has two principal objectives. First it provides an overview of the statistical foundations of Simulation-based inference. This includes the summary and synthesis of the many concepts and results extant in the theoretical literature, the different classes of problems and estimators, the asymptotic properties of these estimators, as well as descriptions of the different simulators in use. Second, the volume provides empirical and operational examples of SBI methods. Often what is missing, even in existing applied papers, are operational issues. Which simulator works best for which problem and why? This volume will explicitly address the important numerical and computational issues in SBI which are not covered comprehensively in the existing literature. Examples of such issues are: comparisons with existing tractable methods, number of replications needed for robust results, choice of instruments, simulation noise and bias as well as efficiency loss in practice.
A clear, up-to-date exposition on a topic of increasing importance
| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| Monography | Library | C1 51 (Browse shelf) | Available | 45735 |
Description
Contents
Resources
Courses
About the Authors
This substantial volume has two principal objectives. First it provides an overview of the statistical foundations of Simulation-based inference. This includes the summary and synthesis of the many concepts and results extant in the theoretical literature, the different classes of problems and estimators, the asymptotic properties of these estimators, as well as descriptions of the different simulators in use. Second, the volume provides empirical and operational examples of SBI methods. Often what is missing, even in existing applied papers, are operational issues. Which simulator works best for which problem and why? This volume will explicitly address the important numerical and computational issues in SBI which are not covered comprehensively in the existing literature. Examples of such issues are: comparisons with existing tractable methods, number of replications needed for robust results, choice of instruments, simulation noise and bias as well as efficiency loss in practice.
A clear, up-to-date exposition on a topic of increasing importance
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