Large dimensional Panel Data Econometrics Testing Estimation And Structural Changes

Download or Read online Large dimensional Panel Data Econometrics Testing Estimation And Structural Changes full in PDF, ePub and kindle. This book written by Chihwa Kao and published by World Scientific which was released on 24 August 2020 with total pages 168. We cannot guarantee that Large dimensional Panel Data Econometrics Testing Estimation And Structural Changes book is available in the library, click Get Book button to download or read online books. Join over 650.000 happy Readers and READ as many books as you like.

Large dimensional Panel Data Econometrics  Testing  Estimation And Structural Changes
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Publisher : World Scientific
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ISBN : 9789811220791
Pages : 168 pages
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Download or Read Online Large dimensional Panel Data Econometrics Testing Estimation And Structural Changes in PDF, Epub and Kindle

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.

Large dimensional Panel Data Econometrics  Testing  Estimation And Structural Changes

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

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