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The Econometrics of multi-dimensional panels : theory and applications / Laszlo Matyas editor.

Contributor(s): Matyas, Laszlo.
Material type: materialTypeLabelBookCall no.: HB141 .E355 2017Series: Advanced studies in theoretical and applied econometrics: v. 50.Publication: Cham, Switzerland : Springer, c2017Description: xix, 456 p. : ill.ISBN: 9783319607825; 3319607820.Subject(s): Econometric models | Game theory | Statistics
Contents:
Foreword; Preface; Acknowledgments; Contents; List of Contributors; Chapter 1 Fixed Effects Models; 1.1 Introduction; 1.2 Models with Different Types of Heterogeneity; 1.3 Least Squares Estimation of the Models; 1.4 Incomplete Panels; 1.5 The Within Estimator; 1.5.1 The Equivalence of the LSDV and the Within Estimator; 1.5.2 Incomplete Panels and the Within Estimator; 1.5.2.1 No Self-flow Data; 1.5.2.2 General Incompleteness; 1.6 Heteroscedasticity and Cross-correlation; 1.6.1 The New Covariance Matrices and the GLS Estimator
1.6.2 Estimation of the Variance Components and the Cross Correlations1.7 Extensions to Higher Dimensions; 1.7.1 Different Forms of Heterogeneity; 1.7.2 Least Squares and the Within Estimators; 1.7.3 Incomplete Panels; 1.8 Varying Coefficients Models; References; Chapter 2 Random Effects Models; 2.1 Introduction; 2.2 Different Model Specifications; 2.2.1 Various Heterogeneity Formulations; 2.2.2 Spectral Decomposition of the Covariance Matrices; 2.3 FGLS Estimation; 2.4 Unbalanced Data; 2.4.1 Structure of the Covariance Matrices; 2.4.2 The Inverse of the Covariance Matrices
2.4.3 Estimation of the Variance Components2.5 Extensions; 2.5.1 4D and Beyond; 2.5.2 Mixed FE-RE Models; 2.6 Testing; 2.7 Conclusion; References; Appendix 1; Example for normalizing with 1: Model (2.14), T →∞; Example for normalizing with √N1N2/A: Model (2.2), N1,N2 →∞; Appendix 2: Proof of formula (2.19); Appendix 3: Inverse of (2.34), and the estimation of the variance components; Chapter 3 Models with Endogenous Regressors; 3.1 Introduction; 3.2 The Hausman-Taylor-like Instrument Variable Estimator; 3.2.1 A Simple Approach; 3.2.2 Sources of Endogeneity; 3.2.3 The Hausman-Taylor Estimator
3.2.3.1 Extending the Hausman-Taylor Two-Stage Least Squares Estimator3.2.3.2 The More Efficient Hausman-Taylor Estimator; 3.2.4 Time Varying Individual Specific Effects; 3.2.5 Properties; 3.2.6 Using External Instruments; 3.3 The Non-linear Generalized Method of Moments Estimator; 3.4 Mixed Effects Models; 3.5 Exogeneity Tests; 3.5.1 Testing for Endogeneity; 3.5.2 Testing for Instrument Validity; 3.5.3 Testing in the Case of Fixed Effects; 3.5.3.1 Improper Model Specifications; 3.5.3.2 Conventional Endogeneity; 3.6 Further Considerations; 3.6.1 Incomplete Data
3.6.2 Notes on Higher-dimensional PanelsReferences; Appendix: Proofs; Proof of Proposition 1; Proof of Corollary 1; Chapter 4 Dynamic Models and Reciprocity; 4.1 Introduction; 4.2 Dynamics; 4.2.1 Estimation; 4.2.2 Monte Carlo Experiments; 4.3 Reciprocity; 4.3.1 Within Estimator; 4.3.2 GMM Estimation; 4.3.3 No Self-flow; 4.4 Combining Dynamics and Reciprocity; 4.4.1 Monte Carlo Experiments; 4.5 Extensions; 4.5.1 Generalized Reciprocity; 4.5.2 Higher Dimensions; References; Appendix; Proof of Proposition 1; Proof of Proposition 2; Chapter 5 Random Coefficients Models; 5.1 Introduction
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Includes bibliographical references and index.

Foreword; Preface; Acknowledgments; Contents; List of Contributors; Chapter 1 Fixed Effects Models; 1.1 Introduction; 1.2 Models with Different Types of Heterogeneity; 1.3 Least Squares Estimation of the Models; 1.4 Incomplete Panels; 1.5 The Within Estimator; 1.5.1 The Equivalence of the LSDV and the Within Estimator; 1.5.2 Incomplete Panels and the Within Estimator; 1.5.2.1 No Self-flow Data; 1.5.2.2 General Incompleteness; 1.6 Heteroscedasticity and Cross-correlation; 1.6.1 The New Covariance Matrices and the GLS Estimator

1.6.2 Estimation of the Variance Components and the Cross Correlations1.7 Extensions to Higher Dimensions; 1.7.1 Different Forms of Heterogeneity; 1.7.2 Least Squares and the Within Estimators; 1.7.3 Incomplete Panels; 1.8 Varying Coefficients Models; References; Chapter 2 Random Effects Models; 2.1 Introduction; 2.2 Different Model Specifications; 2.2.1 Various Heterogeneity Formulations; 2.2.2 Spectral Decomposition of the Covariance Matrices; 2.3 FGLS Estimation; 2.4 Unbalanced Data; 2.4.1 Structure of the Covariance Matrices; 2.4.2 The Inverse of the Covariance Matrices

2.4.3 Estimation of the Variance Components2.5 Extensions; 2.5.1 4D and Beyond; 2.5.2 Mixed FE-RE Models; 2.6 Testing; 2.7 Conclusion; References; Appendix 1; Example for normalizing with 1: Model (2.14), T →∞; Example for normalizing with √N1N2/A: Model (2.2), N1,N2 →∞; Appendix 2: Proof of formula (2.19); Appendix 3: Inverse of (2.34), and the estimation of the variance components; Chapter 3 Models with Endogenous Regressors; 3.1 Introduction; 3.2 The Hausman-Taylor-like Instrument Variable Estimator; 3.2.1 A Simple Approach; 3.2.2 Sources of Endogeneity; 3.2.3 The Hausman-Taylor Estimator

3.2.3.1 Extending the Hausman-Taylor Two-Stage Least Squares Estimator3.2.3.2 The More Efficient Hausman-Taylor Estimator; 3.2.4 Time Varying Individual Specific Effects; 3.2.5 Properties; 3.2.6 Using External Instruments; 3.3 The Non-linear Generalized Method of Moments Estimator; 3.4 Mixed Effects Models; 3.5 Exogeneity Tests; 3.5.1 Testing for Endogeneity; 3.5.2 Testing for Instrument Validity; 3.5.3 Testing in the Case of Fixed Effects; 3.5.3.1 Improper Model Specifications; 3.5.3.2 Conventional Endogeneity; 3.6 Further Considerations; 3.6.1 Incomplete Data

3.6.2 Notes on Higher-dimensional PanelsReferences; Appendix: Proofs; Proof of Proposition 1; Proof of Corollary 1; Chapter 4 Dynamic Models and Reciprocity; 4.1 Introduction; 4.2 Dynamics; 4.2.1 Estimation; 4.2.2 Monte Carlo Experiments; 4.3 Reciprocity; 4.3.1 Within Estimator; 4.3.2 GMM Estimation; 4.3.3 No Self-flow; 4.4 Combining Dynamics and Reciprocity; 4.4.1 Monte Carlo Experiments; 4.5 Extensions; 4.5.1 Generalized Reciprocity; 4.5.2 Higher Dimensions; References; Appendix; Proof of Proposition 1; Proof of Proposition 2; Chapter 5 Random Coefficients Models; 5.1 Introduction

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