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

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

In honor of Dale J. Poirier, experienced editors Ivan Jeliazkov and Justin Tobias bring together a cast of expert contributors to explore the most up-to-date research on econometrics, including subjects such as panel data models, posterior simulation, and Bayesian models.

The first of two volumes in honor of the scholarship of professor Dale JPoirier, this volume consists of 12 chapters on econometrics methods related to identification, limited dependent variables, partial observability, experimentation, and flexible modeling, including both Bayesian and classical contributions to theory and application. The volume begins with an interview with Poirier, then addresses macroeconomic nowcasting using Google probabilities; sentiment-based overlapping community discovery of Reddit's newsfeed users; a psychological model of violence and Israeli and Palestinian fatalities in the Second Intifada; Bayesian methodology for modeling local activation and global connectivity using data on magnetic resonance signals in the brain; robust estimation of ARMA (autoregressive moving average) models with near root cancellation; and the estimation of a stochastic volatility model. Others discuss a novel approach to the modeling of expectation formation and learning in models with time-varying parameters, particularly endogenous gain learning; an approach for checking the sensitivity of predictive modeling to prior hyperparameters; the estimation of a panel model and the use of a Stein-type shrinkage estimator; an out-of-sample Granger causality testing procedure; and the effect of compulsory schooling laws on educational attainment and labor market earnings. Essays were presented at a conference at the U. of California, Irvine, in June 2018, and contributors are data scientists, economists, and other researchers working in Europe, North America, Australia, China, and Saudi Arabia. -- Copyright 2019 * Portland, OR *

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.

SKU	Unavailable
ISBN 13	9781789732429
ISBN 10	1789732425
Title	Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling
Author	Ivan Jeliazkov
Series	Advances In Econometrics
Condition	Unavailable
Binding Type	Hardback
Publisher	Emerald Publishing Limited
Year published	2019-08-30
Number of pages	336
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.