9780521591126

Simulation-based Inference in Econometrics: Methods and Applications

by
  • ISBN13:

    9780521591126

  • ISBN10:

    0521591120

  • Format: Hardcover
  • Copyright: 2000-08-07
  • Publisher: Cambridge University Press
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Supplemental Materials

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  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

Summary

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.

Table of Contents

List of contributors
vii
Foreword
M. Hashem Pesaran
PART I Simulation-based inference in econometrics: methods and applications
Introduction
3(6)
Melvyn Weeks
Simulation-based inference in econometrics: motivation and methods
9(38)
Steven Stern
PART II Microeconometric methods
Introduction
41(6)
Melvyn Weeks
Accelerated Monte Carlo integration: an application to dynamic latent variables models
47(24)
Jean-Francois Richard
Wei Zhang
Some practical issues in maximum simulated likelihood
71(29)
Vassilis A. Hajivassiliou
Bayesian inference for dynamic discrete choice models without the need for dynamic programming
100(32)
John F. Geweke
Michael P. Keane
Testing binomial and multinomial choice models using Cox's non-nested test
132(26)
Melvyn Weeks
Bayesian analysis of the multinomial probit model
158(25)
Robert E. McCulloch
Peter E. Rossi
PART III Time series methods and models
Introduction
179(4)
Til Schuermann
Simulated moment methods for empirical equivalent martingale measures
183(22)
Bent Jesper Christensen
Nicholas M. Kiefer
Exact maximum likelihood estimation of observation-driven econometric models
205(13)
Francis X. Diebold
Til Schuermann
Simulation-based inference in non-linear state-space models: application to testing the permanent income hypothesis
218(17)
Roberto S. Mariano
Hisashi Tanizaki
Simulation-based estimation of some factor models in econometrics
235(20)
Vance L. Martin
Adrian R. Pagan
Simulation-based Bayesian inference for economic time series
255(52)
John F. Geweke
PART IV Other areas of application and technical issues
Introduction
303(4)
Roberto S. Mariano
A comparison of computational methods for hierarchical models in customer survey questionnaire data
307(21)
Eric T. Bradlow
Calibration by simulation for small sample bias correction
328(72)
Christian Gourieroux
Eric Renault
Nizar Touzi
Simulation-based estimation of a non-linear, latent factor aggregate production function
Lee Ohanian
Giovanni L. Violante
Per Krusell
Jose-Victor Rios-Rull359
Testing calibrated general equilibrium models
400(37)
Fabio Canova
Eva Ortega
Simulation variance reduction for bootstrapping
437(21)
Bryan W. Brown
Index 458

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