Panel data econometrics with R

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.

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

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

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