Panel Data Econometrics

Common Factor Analysis for Empirical Researchers

By: Donggyu Sul

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Hardcover | 26 February 2019 | Edition Number 1

At a Glance

Hardcover
166 Pages

Dimensions(cm)
23.4 x 15.6 x 1.12

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In the last 20 years, econometric theory on panel data has developed rapidly, particularly for analyzing common behaviors among individuals over time. Meanwhile, the statistical methods employed by applied researchers have not kept up-to-date. This book attempts to fill in this gap by teaching researchers how to use the latest panel estimation methods correctly.

Almost all applied economics articles use panel data or panel regressions. However, many empirical results from typical panel data analyses are not correctly executed. This book aims to help applied researchers to run panel regressions correctly and avoid common mistakes. The book explains how to model cross-sectional dependence, how to estimate a few key common variables, and how to identify them. It also provides guidance on how to separate out the long-run relationship and common dynamic and idiosyncratic dynamic relationships from a set of panel data.

Aimed at applied researchers who want to learn about panel data econometrics by running statistical software, this book provides clear guidance and is supported by a full range of online teaching and learning materials. It includes practice sections on MATLAB, STATA, and GAUSS throughout, along with short and simple econometric theories on basic panel regressions for those who are unfamiliar with econometric theory on traditional panel regressions.

Industry Reviews

"This book succeeds well by separation and estimation of common factors, while idiosyncratic error components add a new dimension to the panel data literature with cross sectional dependence. The text is accompanied by estimation codes and interesting applications illustrating the power of the generalized models.", Almas Heshmati, Professor of Economics, Joenkoeping University, Sweden

"Donggyu Sul uses recent developments in practical factor analysis to illuminate the interaction of the time-series and cross-section dimensions of panels. The techniques are illustrated with many empirical examples, often based on his research. Matlab, Gauss and Stata codes are provided. The clear and distinctive approach of this book makes it essential reading for everyone working with panel data.", Ron Smith, Birkbeck, University of London, UK

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