Panel data models have become increasingly popular among applied researchers due to their heightened capacity for capturing the complexity of human behavior as compared to cross-sectional or time series data models. As a consequence, richer panel data sets also have become increasingly available. This second edition is a substantial revision of the highly successful first edition of 1986. Recent advances in panel data research are presented in a rigorous and accessible manner and are carefully integrated with the older material. The thorough discussion of theory and the judicious use of…mehr
Panel data models have become increasingly popular among applied researchers due to their heightened capacity for capturing the complexity of human behavior as compared to cross-sectional or time series data models. As a consequence, richer panel data sets also have become increasingly available. This second edition is a substantial revision of the highly successful first edition of 1986. Recent advances in panel data research are presented in a rigorous and accessible manner and are carefully integrated with the older material. The thorough discussion of theory and the judicious use of empirical examples make this book useful to graduate students and advanced researchers in economics, business, sociology, political science, etc. Other specific revisions include the introduction of the notion of strict exogeneity with estimators presented in a generalized method of moments framework, the notion of incidental parameters, more intuitive explanations of pairwise trimming, and discussion of sample selection dynamic panel models.
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Inhaltsangabe
From the contents: 1. Introduction: 1.1 Advantages of panel data; 1.2 Issues involved in utilizing panel data; 1.3 Outline of the monograph; 2. Analysis of covariance: 2.1 Introduction; 2.2 Analysis of covariance; 2.3 An example; 3. Simple regression with variable intercepts: 3.1 Introduction; 3.2 Fixed-effects models: least-squares dummy-variable approach; 3.3 Random-effects models: estimation of variance-components models; 3.4 Fixed effects or random effects; 3.5 Tests for misspecification; 3.6 Models with specific variables and both individual- and time-specific effects; 3.7 Heteroscedasticity; 3.8 Models with serially correlated errors; 3.9 Models with arbitrary error structure "Chamberlain"-approach; 4. Dynamic mode ls with variable intercepts: 4.1 Introduction; 4.2 The covariance estimator; 4.3 Random-effects models; 4.4 An example - demand for natural gas; 4.5 Fixed effects models; 4.6 Estimation of dynamic models with arbitrary correlations in the residuals; 4.7 Fixed effects vector autoregressive models; 5. Simultaneous-equations models: 5.1 Introduction; 5.2 Joint generalized-least squares estimation technique; 5.3 Estimation of structural equations; 5.4 Triangular system; 6. Variable-coefficient models: 6.1 Introduction; 6.2 Coefficients that vary over cross-sectional units; 6.3 Coefficients that vary over time and cross-sectional units; 6.4 Coefficients that evolve over time; 6.5 Coefficients that are functions of other exogenous variables; 6.6 A mixed fixed and random coefficients model; 6.7 Dynamic random coefficients models; 6.8 An example - liquidity constraints and firm investment expenditure; 7. Discrete data: 7.1 Introduction; 7.2 Some discrete-response models; 7.3 The parametric approach to static models with heterogeneity; 7.4 The semiparametric approach to static models; 7.5 Dynamic models; 8. Truncated and censored data: 8.1 Introduction; 8.2 Nonrandomly missing data; 8.3 Tobit models with random individual effects; 8.4 Fixed effects estimator; 8.5 An example: housing expenditure; 8.6 Dynamic Tobit models; 9. Incomplete panel data: 9.1 Estimating distributed lags in short panels; 9.2 Rotating or randomly missing data; 9.3 Pseudo panels (or repeated cross-sectional data); 9.4 Pooling of a single cross-section and a single time series; 10. Miscellaneous topics: 10.1 Simulation methods; 10.2 Panels with large N and T; 10.3 Unit root tests; 10.4 Data with multi-level structures; 10.5 Errors of measurement; 10.6 Modeling cross-sectional dependence; 11. A summary view: 11.1 Introduction; 11.2 Benefits and limitations of panel data; 11.3 Efficiency of the estimates
Preface 1. Introduction 2. Static models with additive effects 3. Dynamic models with additive effects 4. Static simultaneous models with additive effects 5. Dynamic system 6. Qualitative choice models 7. Limited dependent and sample section models 8. Some nonlinear models 9. Miscellaneous topics 10. Interactive effects models 11. Spatial models and cross-sectional dependent data 12. Program evaluation 13. Varying coefficients models 14. Big data analysis.
1. Introduction; 2. Homogeneity test for linear regression models (analysis of covariance); 3. Simple regression with variable intercepts; 4. Dynamic models with variable intercepts; 5. Simultaneous-equations models; 6. Variable-coefficient models; 7. Discrete data; 8. Truncated and censored data; 9. Cross-sectional dependent panel data; 10. Dynamic system; 11. Incomplete panel data; 12. Miscellaneous topics; 13. A summary view.
From the contents: 1. Introduction: 1.1 Advantages of panel data; 1.2 Issues involved in utilizing panel data; 1.3 Outline of the monograph; 2. Analysis of covariance: 2.1 Introduction; 2.2 Analysis of covariance; 2.3 An example; 3. Simple regression with variable intercepts: 3.1 Introduction; 3.2 Fixed-effects models: least-squares dummy-variable approach; 3.3 Random-effects models: estimation of variance-components models; 3.4 Fixed effects or random effects; 3.5 Tests for misspecification; 3.6 Models with specific variables and both individual- and time-specific effects; 3.7 Heteroscedasticity; 3.8 Models with serially correlated errors; 3.9 Models with arbitrary error structure "Chamberlain"-approach; 4. Dynamic mode ls with variable intercepts: 4.1 Introduction; 4.2 The covariance estimator; 4.3 Random-effects models; 4.4 An example - demand for natural gas; 4.5 Fixed effects models; 4.6 Estimation of dynamic models with arbitrary correlations in the residuals; 4.7 Fixed effects vector autoregressive models; 5. Simultaneous-equations models: 5.1 Introduction; 5.2 Joint generalized-least squares estimation technique; 5.3 Estimation of structural equations; 5.4 Triangular system; 6. Variable-coefficient models: 6.1 Introduction; 6.2 Coefficients that vary over cross-sectional units; 6.3 Coefficients that vary over time and cross-sectional units; 6.4 Coefficients that evolve over time; 6.5 Coefficients that are functions of other exogenous variables; 6.6 A mixed fixed and random coefficients model; 6.7 Dynamic random coefficients models; 6.8 An example - liquidity constraints and firm investment expenditure; 7. Discrete data: 7.1 Introduction; 7.2 Some discrete-response models; 7.3 The parametric approach to static models with heterogeneity; 7.4 The semiparametric approach to static models; 7.5 Dynamic models; 8. Truncated and censored data: 8.1 Introduction; 8.2 Nonrandomly missing data; 8.3 Tobit models with random individual effects; 8.4 Fixed effects estimator; 8.5 An example: housing expenditure; 8.6 Dynamic Tobit models; 9. Incomplete panel data: 9.1 Estimating distributed lags in short panels; 9.2 Rotating or randomly missing data; 9.3 Pseudo panels (or repeated cross-sectional data); 9.4 Pooling of a single cross-section and a single time series; 10. Miscellaneous topics: 10.1 Simulation methods; 10.2 Panels with large N and T; 10.3 Unit root tests; 10.4 Data with multi-level structures; 10.5 Errors of measurement; 10.6 Modeling cross-sectional dependence; 11. A summary view: 11.1 Introduction; 11.2 Benefits and limitations of panel data; 11.3 Efficiency of the estimates
Preface 1. Introduction 2. Static models with additive effects 3. Dynamic models with additive effects 4. Static simultaneous models with additive effects 5. Dynamic system 6. Qualitative choice models 7. Limited dependent and sample section models 8. Some nonlinear models 9. Miscellaneous topics 10. Interactive effects models 11. Spatial models and cross-sectional dependent data 12. Program evaluation 13. Varying coefficients models 14. Big data analysis.
1. Introduction; 2. Homogeneity test for linear regression models (analysis of covariance); 3. Simple regression with variable intercepts; 4. Dynamic models with variable intercepts; 5. Simultaneous-equations models; 6. Variable-coefficient models; 7. Discrete data; 8. Truncated and censored data; 9. Cross-sectional dependent panel data; 10. Dynamic system; 11. Incomplete panel data; 12. Miscellaneous topics; 13. A summary view.
Rezensionen
Review of previous edition: 'Researchers will find that the insights that they gain from working through the book's tougher sections are well worth the effort. The book remains an indispensable and comprehensive reference for panel estimation methods.' David C. Ribar, International Journal of Forecasting
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