Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling

Ivan Jeliazkov

ISBN 10: 1838674209 / ISBN 13: 9781838674205

Published by Emerald Publishing Limited, GB, 2019

About this Item

Volume 40 in the Advances in Econometrics series features twenty-three chapters that are split thematically into two parts. Part A presents novel contributions to the analysis of time series and panel data with applications in macroeconomics, finance, cognitive science and psychology, neuroscience, and labor economics. Part B examines innovations in stochastic frontier analysis, nonparametric and semiparametric modeling and estimation, A/B experiments, big-data analysis, and quantile regression.  Individual chapters, written by both distinguished researchers and promising young scholars, cover many important topics in statistical and econometric theory and practice. Papers primarily, though not exclusively, adopt Bayesian methods for estimation and inference, although researchers of all persuasions should find considerable interest in the chapters contained in this work. The volume was prepared to honor the career and research contributions of Professor Dale J. Poirier.  For researchers in econometrics, this volume includes the most up-to-date research across a wide range of topics.

Seller Inventory # LU-9781838674205

Bibliographic Details

• Title: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling
• Publisher: Emerald Publishing Limited, GB
• Publication Date: 2019
• Language: English
• ISBN 10: 1838674209
• ISBN 13: 9781838674205
• Binding: Hardback
• Condition: New
• Weight: 497 grams

About this title

Synopsis

Volume 40 in the Advances in Econometrics series features twenty-three chapters that are split thematically into two parts. Part A presents novel contributions to the analysis of time series and panel data with applications in macroeconomics, finance, cognitive science and psychology, neuroscience, and labor economics. Part B examines innovations in stochastic frontier analysis, nonparametric and semiparametric modeling and estimation, A/B experiments, big-data analysis, and quantile regression.  

Individual chapters, written by both distinguished researchers and promising young scholars, cover many important topics in statistical and econometric theory and practice. Papers primarily, though not exclusively, adopt Bayesian methods for estimation and inference, although researchers of all persuasions should find considerable interest in the chapters contained in this work. The volume was prepared to honor the career and research contributions of Professor Dale J. Poirier.  

For researchers in econometrics, this volume includes the most up-to-date research across a wide range of topics.

About the Author

Ivan Jeliazkov is Associate Professor of Economics at the University of California, Irvine. He has served as Series Editor for Advances in Econometrics since 2010 and has also worked on the editorial boards of JASA/TAS Reviews and the International Journal of Mathematical Modelling and Numerical Optimisation. His research encompasses Bayesian modelling and inference, simulation-based estimation, nonparametric modelling, discrete data analysis, and model comparison.  

Justin Tobias is Professor and Head of the Economics Department at Purdue University. He received his PhD from the University of Chicago in 1999 and has contributed to and served as an Associate Editor for several leading econometrics journals, including the Journal of Applied Econometrics and Journal of Business and Economic Statistics. His work focuses primarily on the development and application of Bayesian microeconometric methods.