Panel data econometrics: common factor analysis for empirical researchers

Intro -- Table of Contents -- Dedication -- Preface -- Acknowledgments -- About the Companion Website -- Chapter 1: Introduction -- 1.1 Panel Data Econometrics: A Gentle Introduction -- 1.2 R for Econometric Computing -- 1.3 plm for the Casual R User -- 1.4 plm for the Proficient R User -- 1.5 plm for the R Developer -- 1.6 Notations -- Chapter 2: The Error Component Model -- 2.1 Notations and Hypotheses -- 2.2 Ordinary Least Squares Estimators -- 2.3 The Generalized Least Squares Estimator -- 2.4 Comparison of the Estimators -- 2.5 The Two‐ways Error Components Model -- 2.6 Estimation of a Wage Equation -- Chapter 3: Advanced Error Components Models -- 3.1 Unbalanced Panels -- 3.2 Seemingly Unrelated Regression -- 3.3 The Maximum Likelihood Estimator -- 3.4 The Nested Error Components Model -- Chapter 4: Tests on Error Component Models -- 4.1 Tests on Individual and/or Time Effects -- 4.2 Tests for Correlated Effects -- 4.3 Tests for Serial Correlation -- 4.4 Tests for Cross‐sectional Dependence -- Chapter 5: Robust Inference and Estimation for Non‐spherical Errors -- 5.1 Robust Inference -- 5.2 Unrestricted Generalized Least Squares -- Chapter 6: Endogeneity -- 6.1 Introduction -- 6.2 The Instrumental Variables Estimator -- 6.3 Error Components Instrumental Variables Estimator -- 6.4 Estimation of a System of Equations -- 6.5 More Empirical Examples -- Chapter 7: Estimation of a Dynamic Model -- 7.1 Dynamic Model and Endogeneity -- 7.2 GMM Estimation of the Differenced Model -- 7.3 Generalized Method of Moments Estimator in Differences and Levels -- 7.4 Inference -- 7.5 More Empirical Examples -- Chapter 8: Panel Time Series -- 8.1 Introduction -- 8.2 Heterogeneous Coefficients -- 8.3 Cross‐sectional Dependence and Common Factors -- 8.4 Nonstationarity and Cointegration -- Chapter 9: Count Data and Limited Dependent Variables.

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

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 model -- 2.4.2 Hierarchical factor model -- 3 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 factors -- 4.2.1 Cross-sectional average (CSA) approach -- 4.2.2 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 practice -- 5 Identification of Common Factors

General introduction -- Capital regulation, efficiency, and risk taking: a spatial panel analysis of US banks -- Panel data in educational research -- Panel data in energy economics* -- Panel data analysis based on lab evidence in auction market experiments -- Maximum likelihood for cross-lagged panel models with fixed effects* -- Panel data in transportation research -- Panel data in banking: research issues and data peculiarities -- Quantile panel estimation of financial contagion effects -- Application of panel data models for empirical economic analysis -- The income-health gradient: evidence from self-reported health and biomarkers in understanding society -- Application in banking: securitization and global banking -- Regional innovation in the United States: a poisson stochastic frontier approach with finite mixture structure -- Making inference of bank managerial preferences about performance: a panel analysis -- The impact of explicit deposit insurance on market discipline* -- Export pricing at the firm level with panel data* -- The relationship between economic growth and democracy: alternative representations of technological change -- Analysis of stochastic dominance rakiing of Chinese income distributions by household attributes -- The neural network production function: panel evidence for the United States -- Index.

2.4.2 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 factors