Simulation-based Inference in Econometrics
Simulation-based Inference in Econometrics
Til Schuermann, AT&T Bell Laboratories, New Jersey
Melvyn J. Weeks, University of Cambridge
AUD$74.50
exc GSTThis 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
- Contributors are leading figures within econometrics with a track record of proven sales success
- Important, original research with major implications for econometric research
Product details
December 2008Paperback
9780521088022
476 pages
230 × 160 × 27 mm
0.69kg
25 tables
Available
Table of Contents
- Part I. Simulation-Based Inference in Econometrics, Methods and Applications: Introduction Melvyn Weeks
- 1. Simulation-based inference in econometrics: motivation and methods Steven Stern
- Part II. Microeconometric Methods: Introduction Melvyn Weeks
- 2. Accelerated Monte Carlo integration: an application to dynamic latent variable models Jean-Francois Richard and Wei Zhang
- 3. Some practical issues in maximum simulated likelihood Vassillis A. Hajivassiliou
- 4. Bayesian inference for dynamic discrete choice models without the need for dynamic programming John Geweke and Miochael Keane
- 6. Bayesian analysis of the multinomial probit model Peter E. Rossi and Robert E. McCulloch
- Part III. Time Series Methods and Models: Introduction Til Schuermann
- 7. Simulated moment methods for empirical equivalent martingale measures Bent Jesper Christensen and Nicholas M. Kiefer
- 8. Exact maximum likelihood estimation of observation-driven econometric models Francis X. Diebold and Til Schuermann
- 9. Simulation-based inference in non-linear state space models: application to testing the permanent income hypothesis Roberto S. Mariano and Hisashi Tanizaki
- 10. Simulation-based estimation of some factor models in econometrics Vance L. Martin and Adrian R. Pagan
- 11. Simulation-based Bayesian inference for economic time series John Geweke
- Part IV. Other Areas of Application and Technical Issues: Introduction Roberto S. Mariano
- 12. A comparison of computational methods for hierarchical methods in customer survey questionnaire data Eric T. Bradlow
- 13. Calibration by simulation for small sample bias correction Christian Gourieroux, Eric Renault and Nizar Touzi
- 14. Simulation-based estimation of a nonlinear, latent factor aggregate production function Lee Ohanian, Giovanni L. Violante, Per Krusell, Jose-Victor Rios-Rull
- 15. Testing calibrated general equilibrium models Fabio Canova and Eva Ortega
- 16. Simulation variance reduction for bootstrapping Bryan W. Brown
- Index.
