Essays in Honor of Aman Ullah
Essays in Honor of Aman Ullah

Author: R. Carter Hill

Publisher: Emerald Group Publishing

Published: 2016-06-29

Total Pages: 680

ISBN-13: 1785607863

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Volume 36 of Advances in Econometrics recognizes Aman Ullah's significant contributions in many areas of econometrics and celebrates his long productive career.

The Oxford Handbook of Panel Data
The Oxford Handbook of Panel Data

Author: Badi Hani Baltagi

Publisher:

Published: 2015

Total Pages: 705

ISBN-13: 0199940045

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The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.

Panel Data Econometrics with R
Panel Data Econometrics with R

Author: Yves Croissant

Publisher: John Wiley & Sons

Published: 2018-08-10

Total Pages: 435

ISBN-13: 1118949188

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Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.

Spatial Econometrics
Spatial Econometrics

Author: J. Paul Elhorst

Publisher: Springer Science & Business Media

Published: 2013-09-30

Total Pages: 125

ISBN-13: 3642403409

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This book provides an overview of three generations of spatial econometric models: models based on cross-sectional data, static models based on spatial panels and dynamic spatial panel data models. The book not only presents different model specifications and their corresponding estimators, but also critically discusses the purposes for which these models can be used and how their results should be interpreted.

Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes
Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes

Author: Feng Qu

Publisher: World Scientific

Published: 2020-08-24

Total Pages: 167

ISBN-13: 9811220794

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This book aims to fill the gap between panel data econometrics textbooks, and the latest development on 'big data', especially large-dimensional panel data econometrics. It introduces important research questions in large panels, including testing for cross-sectional dependence, estimation of factor-augmented panel data models, structural breaks in panels and group patterns in panels. To tackle these high dimensional issues, some techniques used in Machine Learning approaches are also illustrated. Moreover, the Monte Carlo experiments, and empirical examples are also utilised to show how to implement these new inference methods. Large-Dimensional Panel Data Econometrics: Testing, Estimation and Structural Changes also introduces new research questions and results in recent literature in this field.

Panel Data Econometrics
Panel Data Econometrics

Author: Mike Tsionas

Publisher: Academic Press

Published: 2019-06-19

Total Pages: 434

ISBN-13: 0128144319

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Panel Data Econometrics: Theory introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made. - Provides a vast array of empirical applications useful to practitioners from different application environments - Accompanied by extensive case studies and empirical exercises - Includes empirical chapters accompanied by supplementary code in R, helping researchers replicate findings - Represents an accessible resource for diverse industries, including health, transportation, tourism, economic growth, and banking, where researchers are not always econometrics experts

Large Dimensional Factor Analysis
Large Dimensional Factor Analysis

Author: Jushan Bai

Publisher: Now Publishers Inc

Published: 2008

Total Pages: 90

ISBN-13: 1601981449

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Large Dimensional Factor Analysis provides a survey of the main theoretical results for large dimensional factor models, emphasizing results that have implications for empirical work. The authors focus on the development of the static factor models and on the use of estimated factors in subsequent estimation and inference. Large Dimensional Factor Analysis discusses how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy pf observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models.