Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modelling Vol: 40, Part B

Ivan Jeliazkov
University of California, Irvine, USA

Justin Tobias
Purdue University, USA

Product Details
18 Oct 2019
Emerald Publishing Limited
290 pages - 152 x 229mm
Advances in Econometrics
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.
Foreword; Ivan Jeliazkov and Justin Tobias 
1. A Semiparametric Stochastic Frontier Model with Correlated Effects; Gholamreza Hajargasht and William Griffiths 
2. A Bayesian Stochastic Frontier Model with Endogenous Regressors: An Application to the Effect of Division of Labor in Japanese Water Supply Organizations; Eri Nakamura, Takuya Urakami and Kazuhiko Kakamu 
3. An Alternate Parameterization for Bayesian Nonparametric / Semiparametric Regression; Joshua Chan and Justin Tobias 
4. Variable Selection in Sparse Semiparametric Single Index Models; Jianghao Chu, Tae-Hwy Lee and Aman Ullah 
5. Fully Nonparametric Bayesian Additive Regression Trees; Edward George, Prakash Laud, Brent Logan, Robert McCulloch and Rodney Sparapani 
6. Bayesian A/B Inference; John Geweke  
7. Scalable semiparametric inference for the means of heavy-tailed distributions; Hedibert Lopes, Matthew Taddy and Matthew Gardner 
8. Estimation and Applications of Quantile Regression for Binary Longitudinal Data; Mohammad Arshad Rahman and Angela Vossmeyer 
9. On Quantile Estimator in Volatility Model with Non-negative Error Density and Bayesian Perspective; Debajit Dutta, Subhra Sankar Dhar and Amit Mitra 
10. Flexible Bayesian Quantile Regression in Ordinal Models; Mohammad Arshad Rahman and Shubham Karnawat 
11. A Reaction; Dale Poirier
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.

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