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

Author:   Ivan Jeliazkov (University of California, Irvine, USA) ,  Justin Tobias (Purdue University, USA)
Publisher:   Emerald Publishing Limited
Volume:   40, Part A
ISBN:  

9781789732429


Pages:   336
Publication Date:   30 August 2019
Format:   Hardback
Availability:   In Print   Availability explained
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Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling


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Author:   Ivan Jeliazkov (University of California, Irvine, USA) ,  Justin Tobias (Purdue University, USA)
Publisher:   Emerald Publishing Limited
Imprint:   Emerald Publishing Limited
Volume:   40, Part A
Dimensions:   Width: 15.20cm , Height: 2.10cm , Length: 22.90cm
Weight:   0.551kg
ISBN:  

9781789732429


ISBN 10:   1789732425
Pages:   336
Publication Date:   30 August 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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The first of two volumes in honor of the scholarship of professor Dale J. Poirier, 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 *


Author Information

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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