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Full Description
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.
Contents
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
