Large Dimensional Panel Data Econometrics Testing Estimation And Structural Changes

Author : Feng Qu,Chihwa Kao
Publisher : World Scientific
Page : 167 pages
File Size : 55,6 Mb
Release : 2020-08-24
Category : Business & Economics
ISBN : 9789811220791

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Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes by Feng Qu,Chihwa Kao Pdf

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.

Author : Laszlo Matyas
Publisher : Springer
Page : 456 pages
File Size : 53,7 Mb
Release : 2017-07-26
Category : Business & Economics
ISBN : 9783319607832

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The Econometrics of Multi-dimensional Panels by Laszlo Matyas Pdf

This book presents the econometric foundations and applications of multi-dimensional panels, including modern methods of big data analysis. The last two decades or so, the use of panel data has become a standard in many areas of economic analysis. The available models formulations became more complex, the estimation and hypothesis testing methods more sophisticated. The interaction between economics and econometrics resulted in a huge publication output, deepening and widening immensely our knowledge and understanding in both. The traditional panel data, by nature, are two-dimensional. Lately, however, as part of the big data revolution, there has been a rapid emergence of three, four and even higher dimensional panel data sets. These have started to be used to study the flow of goods, capital, and services, but also some other economic phenomena that can be better understood in higher dimensions. Oddly, applications rushed ahead of theory in this field. This book is aimed at filling this widening gap. The first theoretical part of the volume is providing the econometric foundations to deal with these new high-dimensional panel data sets. It not only synthesizes our current knowledge, but mostly, presents new research results. The second empirical part of the book provides insight into the most relevant applications in this area. These chapters are a mixture of surveys and new results, always focusing on the econometric problems and feasible solutions.

Author : Kao Chihwa,Liu Long
Publisher : World Scientific
Page : 180 pages
File Size : 52,5 Mb
Release : 2019-04-10
Category : Business & Economics
ISBN : 9789811200175

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High-dimensional Econometrics And Identification by Kao Chihwa,Liu Long Pdf

In many applications of econometrics and economics, a large proportion of the questions of interest are identification. An economist may be interested in uncovering the true signal when the data could be very noisy, such as time-series spurious regression and weak instruments problems, to name a few. In this book, High-Dimensional Econometrics and Identification, we illustrate the true signal and, hence, identification can be recovered even with noisy data in high-dimensional data, e.g., large panels. High-dimensional data in econometrics is the rule rather than the exception. One of the tools to analyze large, high-dimensional data is the panel data model.High-Dimensional Econometrics and Identification grew out of research work on the identification and high-dimensional econometrics that we have collaborated on over the years, and it aims to provide an up-todate presentation of the issues of identification and high-dimensional econometrics, as well as insights into the use of these results in empirical studies. This book is designed for high-level graduate courses in econometrics and statistics, as well as used as a reference for researchers.

Author : Jushan Bai,Serena Ng
Publisher : Now Publishers Inc
Page : 90 pages
File Size : 47,6 Mb
Release : 2008
Category : Business & Economics
ISBN : 9781601981448

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Large Dimensional Factor Analysis by Jushan Bai,Serena Ng Pdf

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.

Author : Dek Terrell,Tong Li,M. Hashem Pesaran
Publisher : Emerald Group Publishing
Page : 418 pages
File Size : 43,8 Mb
Release : 2020-04-15
Category : Business & Economics
ISBN : 9781789739596

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Essays in Honor of Cheng Hsiao by Dek Terrell,Tong Li,M. Hashem Pesaran Pdf

Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao.

Author : Lajos Horváth
Publisher : Springer Nature
Page : 552 pages
File Size : 52,5 Mb
Release : 2024-06-13
Category : Electronic
ISBN : 9783031516092

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Change Point Analysis for Time Series by Lajos Horváth Pdf

Author : Jeffrey M. Wooldridge
Publisher : MIT Press
Page : 1095 pages
File Size : 54,6 Mb
Release : 2010-10-01
Category : Business & Economics
ISBN : 9780262232586

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Econometric Analysis of Cross Section and Panel Data, second edition by Jeffrey M. Wooldridge Pdf

The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

Author : Cheng Hsiao
Publisher : Cambridge University Press
Page : 563 pages
File Size : 41,6 Mb
Release : 2014-12-08
Category : Business & Economics
ISBN : 9781107038691

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Analysis of Panel Data by Cheng Hsiao Pdf

This book provides a comprehensive, coherent, and intuitive review of panel data methodologies that are useful for empirical analysis. Substantially revised from the second edition, it includes two new chapters on modeling cross-sectionally dependent data and dynamic systems of equations. Some of the more complicated concepts have been further streamlined. Other new material includes correlated random coefficient models, pseudo-panels, duration and count data models, quantile analysis, and alternative approaches for controlling the impact of unobserved heterogeneity in nonlinear panel data models.

Author : Peter Fuleky
Publisher : Springer Nature
Page : 716 pages
File Size : 40,7 Mb
Release : 2019-11-28
Category : Business & Economics
ISBN : 9783030311506

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Macroeconomic Forecasting in the Era of Big Data by Peter Fuleky Pdf

This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.

Author : Yves Croissant,Giovanni Millo
Publisher : John Wiley & Sons
Page : 328 pages
File Size : 50,9 Mb
Release : 2018-08-10
Category : Mathematics
ISBN : 9781118949184

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Panel Data Econometrics with R by Yves Croissant,Giovanni Millo Pdf

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.

Author : Mike Tsionas
Publisher : Academic Press
Page : 432 pages
File Size : 49,6 Mb
Release : 2019-06-19
Category : Business & Economics
ISBN : 9780128144312

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Panel Data Econometrics by Mike Tsionas Pdf

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

Author : Siem Jan Koopman,Neil Shephard
Publisher : Oxford University Press
Page : 389 pages
File Size : 49,7 Mb
Release : 2015
Category : Business & Economics
ISBN : 9780199683666

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Unobserved Components and Time Series Econometrics by Siem Jan Koopman,Neil Shephard Pdf

This volume presents original and up-to-date studies in unobserved components (UC) time series models from both theoretical and methodological perspectives. It also presents empirical studies where the UC time series methodology is adopted. Drawing on the intellectual influence of Andrew Harvey, the work covers three main topics: the theory and methodology for unobserved components time series models; applications of unobserved components time series models; and time series econometrics and estimation and testing. These types of time series models have seen wide application in economics, statistics, finance, climate change, engineering, biostatistics, and sports statistics. The volume effectively provides a key review into relevant research directions for UC time series econometrics and will be of interest to econometricians, time series statisticians, and practitioners (government, central banks, business) in time series analysis and forecasting, as well to researchers and graduate students in statistics, econometrics, and engineering.

Author : John D. Levendis
Publisher : Springer Nature
Page : 493 pages
File Size : 41,7 Mb
Release : 2023-12-23
Category : Business & Economics
ISBN : 9783031373107

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Time Series Econometrics by John D. Levendis Pdf

Revised and updated for the second edition, this textbook allows students to work through classic texts in economics and finance, using the original data and replicating their results. In this book, the author rejects the theorem-proof approach as much as possible, and emphasizes the practical application of econometrics. They show with examples how to calculate and interpret the numerical results. This book begins with students estimating simple univariate models, in a step by step fashion, using the popular Stata software system. Students then test for stationarity, while replicating the actual results from hugely influential papers such as those by Granger & Newbold, and Nelson & Plosser. Readers will learn about structural breaks by replicating papers by Perron, and Zivot & Andrews. They then turn to models of conditional volatility, replicating papers by Bollerslev. Students estimate multi-equation models such as vector autoregressions and vector error-correction mechanisms, replicating the results in influential papers by Sims and Granger. Finally, students estimate static and dynamic panel data models, replicating papers by Thompson, and Arellano & Bond. The book contains many worked-out examples, and many data-driven exercises. While intended primarily for graduate students and advanced undergraduates, practitioners will also find the book useful. “How to best start learning time series econometrics? Learning by doing. This is the ethos of this book. What makes this book useful is that it provides numerous worked out examples along with basic concepts. It is a fresh, no-nonsense, practical approach that students will love when they start learning time series econometrics. I recommend this book strongly as a study guide for students who look for hands-on learning experience." --Professor Sokbae "Simon" Lee, Columbia University, Co-Editor of Econometric Theory and Associate Editor of Econometrics Journal.

Author : Felix Chan,László Mátyás
Publisher : Springer Nature
Page : 385 pages
File Size : 50,6 Mb
Release : 2022-09-07
Category : Business & Economics
ISBN : 9783031151491

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Econometrics with Machine Learning by Felix Chan,László Mátyás Pdf

This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.

Author : Chien-Fu Jeff Lin
Publisher : Unknown
Page : 456 pages
File Size : 46,6 Mb
Release : 1992
Category : Brownian motion processes
ISBN : UCSD:31822005087275

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The Econometrics of Structural Change, Neural Network and Panel Data Analysis by Chien-Fu Jeff Lin Pdf