Simulation-based inference in econometrics: methods and applications

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 ...

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 withou the need for dynamic programming John Geweke and Miochael Keane; 6. Bayesian analysis of the multinomial probit model Peter E. Rossi and Robert E. McCulloc 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…