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Simulation-based inference in econometrics : = methods and applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Simulation-based inference in econometrics :/ Roberto Mariano, Til Schuermann, and Melvyn J. Weeks.
其他題名:	methods and applications /
作者:	Mariano, Roberto S.,
其他作者:	Weeks, Melvyn J.,
面頁冊數:	1 online resource (x, 462 pages) :digital, PDF file(s). :
附註:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
標題:	Economics - Simulation methods. -
電子資源:	https://doi.org/10.1017/CBO9780511751981
ISBN:	9780511751981 (ebook)

Simulation-based inference in econometrics : = methods and applications /
Mariano, Roberto S.,

Simulation-based inference in econometrics :methods and applications /Roberto Mariano, Til Schuermann, and Melvyn J. Weeks. - 1 online resource (x, 462 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Simulation-based inference in econometrics : motivation and methods / Steven Stern -- Accelerated Monte Carlo integration : an application to dynamic latent variables models / Jean-François Richard and Wei Zhang -- Some practical issues in maximum simulated likelihood / Vassilis A. Hajivassiliou -- Bayesian inference for dynamic discrete choice models without the need for dynamic programming / John F. Geweke and Michael P. Keane -- Testing binomial and multinomial choice models using Cox's non-nested test / Melvyn Weeks -- Bayesian analysis of the multinomial probit model / Robert E. McCulloch and Peter E. Rossi -- Simulated moment methods for empirical equivalent martingale measures / Bent Jesper Christensen and Nicholas M. Kiefer -- Exact maximum likelihood estimation of observation-driven econometric models / Francis X. Diebold and Til Schuermann -- Simulation-based inference in non-linear state-space models : application to testing the permanent income hypothesis / Roberto S. Mariano and Hisashi Tanizaki -- Simulation-based estimation of some factor models in econometrics / Vance L. Martin and Adrian R. Pagan -- Simulation-based Bayesian inference for economic time series / John F. Geweke -- A comparison of computational methods for hierarchical models in customer survey questionnaire data / Eric T. Bradlow -- Calibration by simulation for small sample bias correction / Christian Gourieroux, Eric Renault, and Nizar Touzi -- Simulation-based estimation of a non-linear, latent factor aggregate production function / Lee Ohanian [and others] -- Testing calibrated general equilibrium models / Fabio Canova and Eva Ortega -- Simulation variance reduction for bootstrapping / Bryan W. Brown.

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.

ISBN: 9780511751981 (ebook)Subjects--Topical Terms:

788082
Economics
--Simulation methods.

LC Class. No.: HB141 / .M353 2000

Dewey Class. No.: 330/.01/5195

Simulation-based inference in econometrics : = methods and applications /
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505 0 $a Simulation-based inference in econometrics : motivation and methods / Steven Stern -- Accelerated Monte Carlo integration : an application to dynamic latent variables models / Jean-François Richard and Wei Zhang -- Some practical issues in maximum simulated likelihood / Vassilis A. Hajivassiliou -- Bayesian inference for dynamic discrete choice models without the need for dynamic programming / John F. Geweke and Michael P. Keane -- Testing binomial and multinomial choice models using Cox's non-nested test / Melvyn Weeks -- Bayesian analysis of the multinomial probit model / Robert E. McCulloch and Peter E. Rossi -- Simulated moment methods for empirical equivalent martingale measures / Bent Jesper Christensen and Nicholas M. Kiefer -- Exact maximum likelihood estimation of observation-driven econometric models / Francis X. Diebold and Til Schuermann -- Simulation-based inference in non-linear state-space models : application to testing the permanent income hypothesis / Roberto S. Mariano and Hisashi Tanizaki -- Simulation-based estimation of some factor models in econometrics / Vance L. Martin and Adrian R. Pagan -- Simulation-based Bayesian inference for economic time series / John F. Geweke -- A comparison of computational methods for hierarchical models in customer survey questionnaire data / Eric T. Bradlow -- Calibration by simulation for small sample bias correction / Christian Gourieroux, Eric Renault, and Nizar Touzi -- Simulation-based estimation of a non-linear, latent factor aggregate production function / Lee Ohanian [and others] -- Testing calibrated general equilibrium models / Fabio Canova and Eva Ortega -- Simulation variance reduction for bootstrapping / Bryan W. Brown.
520 $a 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.
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