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

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.

This work presents recent work in statistical and economic theory and practice; most of the papers apply Bayesian methods for estimation and inference. The book provides 11 chapters by established and emerging scholars, including two chapters on stochastic frontier models, three chapters on quantile regression, a set of three chapters on semiparametric and nonparametric modeling, and two chapters on developing methodologies for making quick and reliable inference in A/B experiments. The book will be of interest to researchers in econometrics. Distributed in North America by Turpin Distribution.

• 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