TY  - BOOK
AU  - Baltagi,Badi Hani
TI  - Econometric analysis of panel data
SN  - 0470518863
U1  - 330.015195 
PY  - 2009///, c2008
CY  - Chichester, UK, Hoboken, NJ
PB  - John Wiley & Sons
KW  - Econometría
N1  - Incluye bibliografía; 12.3 Panel Unit Root Tests Allowing for Cross-Sectional Dependence. 12.4 Spurious Regression in Panel Data. 12.5 Panel Cointegration Tests. 12.6 Estimation and Inference in Panel Cointegration Models. 12.7 Empirical Example: Purchasing Power Parity. 12.8 Further Reading. Notes. Problems. References. Index; 9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data. 9.6 The Unbalanced Nested Error Component Model. Notes. Problems. 10. Special Topics. 10.1 Measurement Error and Panel Data. 10.2 Rotating Panels. 10.3 Pseudo-panels. 10.4 Alternative Methods of Pooling Time Series of Cross-section Data. 10.5 Spatial Panels. 10.6 Short-run vs Long-run Estimates in Pooled Models. 10.7 Heterogeneous Panels. 10.8 Count Panel Data. Notes. Problems. 11. Limited Dependent Variables and Panel Data. 11.1 Fixed and Random Logit and Probit Models. 11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data. 11.3 Dynamic Panel Data Limited Dependent Variable Models. 11.4 Selection Bias in Panel Data. 11.5 Censored and Truncated Panel Data Models. 11.6 Empirical Applications. 11.7 Empirical Example: Nurses Labor Supply. 11.8 Further Reading. Notes. Problems. 12. Nonstationary Panels. 12.1 Introduction. 12.2 Panel Unit Root Tests Assuming Cross-Sectional Independence.^; Preface. 1. Introduction. 1.1 Panel Data: Some Examples. 1.2 Why Should We Use Panel Data? Their Benefits and Limitations. Note. 2. The One-way Error Component Regression Model. 2.1 Introduction. 2.2 The Fixed Effects Model. 2.3 The Random Effects Model. 2.4 Maximum Likelihood Estimation. 2.5 Prediction. 2.6 Examples. 2.7 Selected Applications. 2.8 Computational Note. Notes. Problems. 3. The Two-way Error Component Regression Model. 3.1 Introduction. 3.2 The Fixed Effects Model. 3.3 The Random Effects Model. 3.4 Maximum Likelihood Estimation. 3.5 Prediction. 3.6 Examples. 3.7 Selected Applications. Notes. Problems. 4. Test of Hypotheses with Panel Data. 4.1 Tests for Poolability of the Data. 4.2 Tests for Individual and Time Effects. 4.3 Hausman?s Specification Test. 4.4 Further Reading. Notes. Problems. 5. Heteroskedasticity and Serial Correlation in the Error Component Model. 5.1 Heteroskedasticity. 5.2 Serial Correlation. Notes. Problems. 6.^; Seemingly Unrelated Regressions with Error Components. 6.1 The One-way Model. 6.2 The Two-way Model. 6.3 Applications and Extensions. Problems. 7. Simultaneous Equations with Error Components. 7.1 Single Equation Estimation. 7.2 Empirical Example: Crime in North Carolina. 7.3 System Estimation. 7.4 The Hausman and Taylor Estimator. 7.5 Empirical Example: Earnings Equation Using PSID Data. 7.6 Further Reading and Extensions. Notes. Problems. 8. Dynamic Panel Data Models. 8.1 Introduction. 8.2 The Arellano and Bond Estimator. 8.3 The Arellano and Bover Estimator. 8.4 The Ahn and Schmidt Moment Conditions. 8.5 The Blundell and Bond System GMM Estimator. 8.6 The Keane and Runkle Estimator. 8.7 Further Developments. 8.8 Empirical Examples. 8.9 Further Reading. Notes. Problems. 9. Unbalanced Panel Data Models. 9.1 Introduction. 9.2 The Unbalanced One-way Error Component Model. 9.3 Empirical Example: Hedonic Housing. 9.4 The Unbalanced Two-way Error Component Model.^
UR  - http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016284736&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
UR  - http://www.loc.gov/catdir/enhancements/fy0814/2008004962-t.html
ER  -