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.
