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Ivan Jeliazkov, Justin Tobias - Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling, Inbunden

Inbunden, Engelska, 2019

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

Av Ivan Jeliazkov, Justin Tobias

Del i serien Advances in Econometrics

1199 kr

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Beskrivning
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.
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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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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.

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