Simulation-based Inference in Econometrics
Methods and Applications

An overview of the techniques and practices involved in simulation-based inference.

Roberto Mariano (Edited by), Til Schuermann (Edited by), Melvyn J. Weeks (Edited by)

9780521591126, Cambridge University Press

Hardback, published 20 July 2000

476 pages, 25 tables
23.6 x 15.9 x 3.9 cm, 0.835 kg

"This book would be a valuable reference for empirical researchers interested in applying simulation-based methods." JASA

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.

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.

Subject Areas: Econometrics [KCH]