European Parliament Library

Panel data econometrics, common factor analysis for empirical researchers, Donggyu Sul

Panel data econometrics, common factor analysis for empirical researchers, Donggyu Sul
index present
Literary Form
non fiction
Main title
Panel data econometrics
Nature of contents
Oclc number
Responsibility statement
Donggyu Sul
Sub title
common factor analysis for empirical researchers
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
Table Of Contents
Cover; Half Title; Title; Copyright; CONTENTS; List of figures; List of tables; Preface; 1 Basic structure of panel data; 1.1 Meaning of fixed effect; 1.1.1 Fixed effects with non-trended data; 1.1.2 Fixed effects with trended panel data; 1.2 Meaning of common components; 1.2.1 Aggregation or macro factor; 1.2.2 Source of cross-sectional dependence; 1.2.3 Central location parameter; 1.3 Meaning of idiosyncratic components; 2 Statistical models for cross-sectional dependence; 2.1 Spatial dependence; 2.2 Gravity model; 2.3 Common factor approach; 2.4 Other variations; 2.4.1 Dynamic factor model2.4.2 Hierarchical factor model3 Factor number identification; 3.1 A step-by-step procedure for determining the factor number; 3.2 Information criteria and alternative methods; 3.3 Standardization and prewhitening; 3.4 Practice: factor number estimation; 3.4.1 STATA practice with crime rates; 3.4.2 STATA practice with price indices; 3.4.3 Practice with GAUSS; 3.4.4 Practice with MATLAB; 4 Decomposition of panel: estimation of common and idiosyncratic components; 4.1 Measurement of accuracy: order in probability; 4.2 Estimation of the common factors4.2.1 Cross-sectional average (CSA) approach4.2.2 Principal component estimator; 4.2.3 Comparison between two estimators for the common factors; 4.3 Estimation of the idiosyncratic components; 4.4 Variance decomposition; 4.5 Cross-sectional dependence and level of aggregation; 4.5.1 General static factor structure; 4.5.2 Hierarchical factor structure; 4.6 Practice: common factors estimation; 4.6.1 GAUSS practice I: principal component estimation; 4.6.2 GAUSS practice II: standardization and estimation of PC factors; 4.6.3 MATLAB practice; 4.6.4 STATA practice5 Identification of Common Factors5.1 Difference between statistical and latent factors; 5.2 Asymptotically weak factors approach; 5.2.1 Single-factor case; 5.2.2 Multi-factor case; 5.2.3 Some tips to identify latent factors; 5.2.4 Application: testing homogeneity of factor loadings; 5.3 Residual-based approach; 5.4 Empirical example: exchange rates; 5.5 Practice: identifying common factors; 5.5.1 MATLAB practice I: leadership model; 5.5.2 MATLAB practice II: multiple variables as single factor; 5.5.3 Practice with GAUSS; 5.5.4 Practice with STATA; 6 Static and dynamic relationships6.1 Static and dynamic relationship under cross-sectional independence6.1.1 Spurious cross-sectional regression; 6.1.2 Spurious pooled OLS estimator; 6.1.3 Time series and panel-fixed effect regressions; 6.1.4 Between-group estimator; 6.2 Static and dynamic relationship under cross-sectional dependence; 6.2.1 Homogeneous factor loadings; 6.2.2 Heterogeneous factor loadings: factor-augmented panel regression; 6.2.3 Cross-sectional regressions with nonstationary common factors; 6.3 Practice: factor-augmented and aggregation regressions; 6.3.1 Practice with GAUSS I: common-dynamic relationship
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