Simulation-based Inference in Econometrics

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.