| Autore |
Matyas Laszlo
|
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa |
Cham : , : Springer International Publishing AG, , 2024
|
| Descrizione fisica |
1 online resource (561 pages)
|
| Altri autori (Persone) |
NerloveMarc
|
| Collana |
Advanced Studies in Theoretical and Applied Econometrics Series
|
| ISBN |
3-031-49849-6
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Intro -- Foreword -- Preface -- Acknowledgements -- 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 Model Selection -- 1.5 Incomplete Panels -- 1.6 The Within Estimator -- 1.6.1 The Equivalence of the LSDV and theWithin Estimator -- 1.6.2 Incomplete Panels and the Within Estimator -- 1.6.2.1 No Self-flow Data -- 1.6.2.2 General Incompleteness -- 1.7 Heteroscedasticity and Cross-correlation -- 1.7.1 The New Covariance Matrices and the GLS Estimator -- 1.7.2 Estimation of the Variance Components and the Cross-correlations -- 1.8 Extensions to Higher Dimensions -- 1.8.1 Different Forms of Heterogeneity -- 1.8.2 Least Squares and the Within Estimators -- 1.8.3 Incomplete Panels -- 1.9 Varying Coefficients Models -- References -- Chapter 2 When and How Much Do Fixed Effects Matter? -- 2.1 Introduction -- 2.2 Effect of the Fixed Effects on Parameters of Interest -- 2.2.1 Misspecification in 2D -- 2.2.2 Misspecification in 3D and Higher Dimensions -- 2.3 Monte Carlo Evidence -- 2.4 Machine Learning Tools to Learn the Fixed Effects Structure -- 2.4.1 The Impossibility of Learning the Fully Interacting Fixed Effects -- 2.4.2 Learning High-dimensional Fixed Effects Structures -- 2.5 Conclusion -- Appendix -- Properties of the Residual Maker -- 2D case -- Proof of Corollary 2.1 -- Example for the Autoregressive Process in 3D -- References -- Chapter 3 Random Effects Models -- 3.1 Introduction -- 3.2 Different Model Specifications -- 3.2.1 Various Heterogeneity Formulations -- 3.2.2 Spectral Decomposition of the Covariance Matrices -- 3.3 FGLS Estimation -- 3.4 Unbalanced Data -- 3.4.1 Structure of the Covariance Matrices -- 3.4.2 The Inverse of the Covariance Matrices -- 3.4.3 Estimation of the Variance Components.
3.5 Extensions -- 3.5.1 4D and Beyond -- 3.5.2 Mixed FE-RE Models -- 3.6 Testing -- 3.7 Conclusion -- Appendix 1 -- Example for normalizing with 1: Model (3.14), → ∞ -- Example for normalizing with √ 1 2/ : Model (3.2), 1, 2 → ∞ -- Appendix 2: Proof of formula (3.19) -- Appendix 3: Inverse of (3.34), and the estimation of the variance components -- References -- Chapter 4 Estimation of Sparse Variance-Covariance Matrix -- 4.1 Introduction -- 4.2 Basic Methods -- 4.3 Multi-dimensional Panel Data and Sparse Variance-Covariance Matrix -- 4.4 Estimating Variance-Covariance Matrix -- 4.4.1 Estimating Variance-Covariance Matrices in Higher Dimensions -- 4.4.1.1 Case 1: -- 4.4.1.2 Case 2: -- 4.4.1.3 Case 3: -- 4.4.1.4 Case 4: -- 4.4.2 Shrinkage Approximation -- 4.4.3 Regularistion -- 4.5 Testing for Misspecification in the Unobserved Heterogeneity -- 4.5.1 Finite Sample Performance of the Misspecification Test -- 4.6 Further Considerations -- 4.6.1 Unbalanced Panel -- 4.6.2 Higher Dimension -- 4.6.3 Computation -- Appendix -- References -- Chapter 5 Models with Endogenous Regressors -- 5.1 Introduction -- 5.2 The Hausman-Taylor-like Instrument Variable Estimator -- 5.2.1 A Simple Approach -- 5.2.2 Sources of Endogeneity -- 5.2.3 The Hausman-Taylor Estimator -- 5.2.3.1 Extending the Hausman-Taylor Two-Stage Least Squares Estimator -- 5.2.3.2 The More Efficient Hausman-Taylor Estimator -- 5.2.4 Time Varying Individual Specific Effects -- 5.2.5 Properties -- 5.2.6 Common Correlated Effects Pooled with Hausman-Taylor -- 5.2.7 Using External Instruments -- 5.3 The Non-linear Generalized Method of Moments Estimator -- 5.4 Mixed Effects Models -- 5.5 Exogeneity Tests -- 5.5.1 Testing for Endogeneity -- 5.5.2 Testing for Instrument Validity -- 5.5.3 Testing in the Case of Fixed Effects -- 5.5.3.1 Improper Model Specifications -- 5.5.3.2 Conventional Endogeneity.
5.6 Further Considerations -- 5.6.1 Incomplete Data -- 5.6.2 Notes on Higher-dimensional Panels -- Appendix: Proofs -- Proof of Proposition 5.1 -- Proof of Corollary 5.1 -- References -- Chapter 6 Dynamic Models and Reciprocity -- 6.1 Introduction -- 6.2 Dynamics -- 6.2.1 Estimation -- 6.2.2 Monte Carlo Experiments -- 6.3 Reciprocity -- 6.3.1 Within Estimator -- 6.3.2 Estimation -- 6.3.3 No Self-flow -- 6.4 Combining Dynamics and Reciprocity -- 6.4.1 Monte Carlo Experiments -- 6.5 Extensions -- 6.5.1 Generalized Reciprocity -- 6.5.2 Higher Dimensions -- Appendix -- References -- Chapter 7 Random Coefficients Models -- 7.1 Introduction -- 7.2 The Linear Model for Three Dimensions -- 7.2.1 The Model -- 7.3 Identification and Estimation -- 7.3.1 Feasible Generalized Least Squares (FGLS) -- 7.3.1.1 Variance-Covariance Estimation -- 7.3.1.2 Method 1: Using Within Dimensions Variation -- 7.3.1.3 Method 2: Using the Overall Variation -- 7.3.1.4 Minimum Norm Quadratic Unbiased Estimation (MINQUE) -- 7.3.1.5 Statistical properties of the Estimators -- 7.3.2 Maximum Likelihood Estimation -- 7.3.2.1 The Unrestricted Maximum Likelihood -- 7.3.2.2 Restricted Maximum Likelihood Estimation -- 7.4 Inference: Varying (Random) or Constant Coefficients? -- 7.4.1 Testing for Methods 1 and 2 -- 7.4.1.1 Test in Steps -- 7.4.1.2 Joint Test for the Presence of Random Elements -- 7.4.2 Testing in the Case of MLE -- 7.5 Prediction of the Coefficients -- 7.6 Bayesian Approach -- 7.7 Extensions within the Linear Model -- 7.7.1 Alternative Model Formulations -- 7.7.2 Incomplete Panels -- 7.7.3 Cross-Section Dependence -- 7.7.4 Random Coefficients Correlated with the Explanatory Variables -- 7.7.5 Some Random and Some 'Fixed' Coefficients? -- 7.7.6 Higher Dimensions -- 7.8 Misspecification of the Variance-Covariance Matrix -- 7.9 Non Linear Extension: RC Probit Model.
7.10 A Simulation Experiment -- 7.11 Conclusions -- References -- Chapter 8 Nonparametric Models with Random Effects -- 8.1 Introduction -- 8.2 The Pooled Local Linear Estimator -- 8.3 Two-step Local Linear Estimator -- 8.3.1 Weighted Local Linear Estimator -- 8.3.2 Two-step Estimator -- 8.4 Pairwise Random Effects -- 8.4.1 Cases (i)-(iii): The Sample Size Increases in One Index Only -- 8.4.2 Cases (iv)-(vi): The Sample Size Increases in Two out of the Three Indices -- 8.4.3 Case (vii): The Sample Size Increases in All Three Indices -- 8.5 Some Extensions -- 8.5.1 Mixed Fixed and Random Effects Models -- 8.5.2 Four and Higher-dimensional Cases -- 8.5.3 Fixed Effects Models -- 8.6 Conclusion -- Acknowledgements -- Appendix -- References -- Chapter 9 Nonparametric Models with Fixed Effects -- 9.1 Introduction -- 9.2 Random Effects Models -- 9.3 Fixed Effects Models -- 9.3.1 Parametric Estimation -- 9.3.2 Nonparametric Estimation -- 9.3.2.1 Within Estimator -- 9.3.2.2 Pairwise Estimator -- 9.3.3 Semiparametric Estimation -- 9.4 Inference -- 9.4.1 A More General Central Limit Theorem for U-Statistics -- 9.4.2 Parametric vs Nonparametric -- 9.4.3 Parametric vs Varying-Coefficient -- 9.4.4 Testing for Relevance -- 9.4.5 Hausman Test -- 9.5 Potential Extensions -- 9.6 Empirical Illustration: Environmental Kuznets Curve -- 9.6.1 Data -- 9.6.2 Specification Tests -- 9.6.3 Gradient Estimates -- 9.7 Conclusion -- Acknowledgements -- Appendix -- Assumptions -- Asymptotic Results for the Within Estimator -- Lemmas with Corresponding Proofs -- References -- Chapter 10 Multi-dimensional Panels in Quantile Regression Models -- 10.1 Introduction -- 10.2 Fixed Effects Models -- 10.2.1 Estimation and Implementation -- 10.2.2 Inference Procedures -- 10.2.2.1 A random-weighted bootstrap method -- Practical implementation -- Percentile confidence interval.
Variance-covariance matrix estimation -- 10.2.3 Smoothed Quantile Regression Panel Data -- Bias Correction Analytical Method -- Jackknife -- 10.3 Random Effects Models -- 10.3.1 Model -- 10.3.2 Estimation and Implementation -- 10.3.3 Inference Procedures -- 10.3.4 Correlated Random Effects Models -- 10.4 Quantile Models for Network Data -- 10.5 Specific Guidelines for Practitioners -- References -- Chapter 11 Multi-Dimensional Models for Spatial Panels -- 11.1 Introduction -- 11.2 Spatial Models -- 11.2.1 The Baseline Model -- 11.2.2 Unobserved Heterogeneity -- 11.3 Spatial Estimation Methods -- 11.3.1 Maximum Likelihood Estimation -- 11.3.2 GM, FGLS and Instrumental Variables Approaches -- The GM Spatial FGLS Estimator -- 11.4 Testing for Spatial Dependence -- 11.5 Prediction with Spatial Models -- 11.6 Some Further Topics -- 11.6.1 Heterogenous Coefficients Spatial Models -- 11.6.2 Time-Space Models -- 11.7 Conclusion -- References -- Chapter 12 The Econometrics of Gravity Models in International Trade -- 12.1 Introduction -- 12.2 Generic Theoretical Background -- 12.3 Specific Problems with Estimating Gravity Models -- 12.3.1 Heteroskedasticity -- 12.3.2 Modelling the Mass Point at Zero Bilateral Trade Flows -- 12.3.3 Dynamics -- 12.3.4 Spatial Data: Interdependence of Bilateral Trade Flows Conditional on Exogenous Determinants -- 12.3.5 Endogenous Regressors -- 12.3.6 Ratio Estimators -- 12.3.7 Binary Regressors and Event-study Designs -- 12.3.8 High-dimensional Data Designs -- 12.3.9 Nonparametric Analysis and Machine Learning -- 12.3.10 Inference -- 12.4 Conclusion -- References -- Chapter 13 Modelling Housing Using Multi-dimensional Panel Data -- 13.1 Introduction -- 13.2 Discrete Choice Models and Hedonic Price Functions: A Quick Overview -- 13.3 Multi-dimensional Models of Housing Hedonic Price Functions: Some Examples.
13.4 Multi-dimensional Models of Residential Mobility and Location Choice: Some Examples.
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| Record Nr. | UNINA-9910831019303321 |
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Analysis of economic time series : a synthesis / Marc Nerlove, David M. Grether, José L. Carvalho
