The econometrics of multi-dimensional panels: theory and applications
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| Ausgabe: | Second edition |
| Schriftenreihe: | Advanced Studies in Theoretical and Applied Econometrics
Volume 54 |
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| Beschreibung: | xxi, 551 Seiten Diagramme |
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| 245 | 1 | 0 | |a The econometrics of multi-dimensional panels |b theory and applications |c Laszlo Matyas, editor ; foreword by Marc Nerlove |
| 250 | |a Second edition | ||
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| 650 | 4 | |a Data Analysis and Big Data | |
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Contents 1 2 Fixed Effects Models. Laszlo Balâzsi, Laszlo Mâtyâs, and Tom Wansbeek 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 the Within Estimator . 1.6.2 Incomplete Panels and the Within Estimator. 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. When and How Much Do Fixed Effects Matter?. Felix Chan, Laszlo Mâtyâs and Ägoston Reguly 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 . 53 1 1 4 8 12 13 15 15 16 21 22 23 26 26 27 28 29 33 39 39 40 41 46 50 53 xi
xii 3 4 Contents 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. 56 57 58 58 58 59 59 59 Random Effects Models . Laszlo Balâzsi, Badi H. Baltagi, Laszlo Mâtyâs and Daria Pus 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), T — co. Example for normalizing with VN1N2/A: Model (3.2), Νι,Νϊ — °0. Appendix 2: Proof of formula (3.19). Appendix 3: Inverse of (3.34), and the estimation of the variance components. 93 References. 61 61 62 64 66 71 77 77 80 82 84 84 86 88 90 90 90 91 92 95 Estimation of Sparse Variance-Covariance Matrix. 99 Felix Chan and Ramzi Chariag 4.1 Introduction. 99 4.2 Basic
Methods. 101 4.3 Multi-dimensional Panel Data and Sparse Variance-Covariance Matrix . 105 4.4 Estimating Variance-Covariance Matrix . 109 4.4.1 Estimating Variance-Covariance Matrices in Higher Dimensions. 109 4.4.2 Shrinkage Approximation. 115 4.4.3 119 4.5 Testing for Misspecification in the Unobserved Heterogeneity . 121 4.5.1 Finite Sample Performance of the Misspecification Test. 124
Contents xiii Further Considerations. 128 4.6.1 Unbalanced Panel.128 4.6.2 Higher Dimension. 129 4.6.3 Computation . 129 Appendix.129 References.129 4.6 5 Models with Endogenous Regressors. 133 Lâszlô Balâzsi, Maurice J.G. Bun, Felix Chan and Mark N. Harris 5.1 Introduction. 134 5.2 The Hausman-Taylor-like Instrument Variable Estimator.136 5.2.1 A Simple Approach. 136 5.2.2 Sources of Endogeneity. 137 5.2.3 The Hausman-Taylor Estimator. 138 5.2.4 Time Varying Individual Specific Effects.143 5.2.5 Properties. 147 5.2.6 Common Correlated Effects Pooled with Hausman-Taylor 149 5.2.7 Using External Instruments. 152 5.3 The Non-linear Generalized Method
of Moments Estimator. 153 5.4 Mixed Effects Models .155 5.5 Exogeneity Tests. 158 5.5.1 Testing for Endogeneity . 158 5.5.2 Testing for Instrument Validity. 159 5.5.3 Testing in the Case of Fixed Effects. 160 5.6 Further Considerations.162 5.6.1 Incomplete Data . 162 5.6.2 Notes on Higher-dimensional Panels. 162 Appendix: Proofs. 163 Proof of Proposition 5.1. 163 Proof of Corollary 5.1 . 167 References. 167 6 Dynamic Models and Reciprocity . 171 Maurice J.G. Bun, Felix Chan, Mark N. Harris and Wei Ern (Ben) Yeo 6.1 Introduction.171 6.2
Dynamics. 173 6.2.1 Estimation. 174 6.2.2 Monte Carlo Experiments. 177 6.3 Reciprocity. 180 6.3.1 Within Estimator. 180 6.3.2 Estimation. 182 6.3.3 No Self-flow. 186 6.4 Combining Dynamics and Reciprocity. 187 6.4.1 Monte Carlo Experiments. 189 6.5 Extensions. 190 6.5.1 Generalized Reciprocity. 190
xiv Contents 6.5.2 Higher Dimensions. 192 Appendix.192 References.192 7 Random Coefficients Models. 197 Monika Avila Marquez, Jaya Krishnakumar, and Laszlo Balâzsi 7.1 Introduction . 197 7.2 The Linear Model for Three Dimensions. 200 7.2.1 The Model. 200 7.3 Identification and Estimation. 202 7.3.1 Feasible GeneralizedLeast Squares (FGLS). 202 7.3.2 Maximum Likelihood Estimation. 211 7.4 Inference: Varying (Random) or Constant Coefficients? . 213 7.4.1 Testing for Methods 1 and 2. 213 7.4.2 Testing in the Case of MLE. 217 7.5 Prediction of the Coefficients . 217 7.6 Bayesian Approach. 219 7.7 Extensions within the Linear
Model.220 7.7.1 Alternative Model Formulations. 220 7.7.2 Incomplete Panels. 222 7.7.3 Cross-Section Dependence. 223 7.7.4 Random Coefficients Correlated with the Explanatory Variables. 225 7.7.5 Some Random and Some ‘Fixed’Coefficients?. 226 7.7.6 Higher Dimensions. 227 7.8 Misspecification of the Variance-Covariance Matrix. 228 7.9 Non Linear Extension: RC Probit Model.230 7.10 A Simulation Experiment . 232 7.11 Conclusions.234 References. 235 8 Nonparametric Models with Random Effects. 239 Yiguo Sun, Wei Lin and Qi Li 8.1 Introduction. 239 8.2 The Pooled Local Linear Estimator. 241 8.3 Two-step Local Linear Estimator .248 8.3.1 Weighted Local Linear
Estimator. 250 8.3.2 Two-step Estimator. 251 8.4 Pairwise Random Effects. 258 8.4.1 Cases (i)-(iii): The Sample Size Increases in One Index Only.260 8.4.2 Cases (iv)-(vi): The Sample Size Increases in Two out of the Three Indices. 263 8.4.3 Case (vii): The Sample Size Increases in All Three Indices 268 8.5 Some Extensions. 277 8.5.1 Mixed Fixed and Random Effects Models. 277
Contents XV 8.5.2 Four and Higher-dimensional Cases. 278 8.5.3 Fixed Effects Models. 279 8.6 Conclusion.280 Acknowledgements. 280 Appendix.281 References. 283 9 Nonparametric Models with Fixed Effects . 285 Daniel J. Henderson and Alexandra Soberon 9.1 Introduction.285 9.2 Random Effects Models. 287 9.3 Fixed Effects Models. 287 9.3.1 Parametric Estimation. 288 9.3.2 Nonparametric Estimation. 290 9.3.3 Semiparametric Estimation . 298 9.4 Inference. 300 9.4.1 A More General Central Limit Theorem for U-Statistics .301 9.4.2 Parametric vs
Nonparametric. 301 9.4.3 Parametric vs . .303 9.4.4 Testing for Relevance. 304 9.4.5 Hausman Test. 306 9.5 Potential Extensions. 307 9.6 Empirical Illustration: Environmental Kuznets Curve. 308 9.6.1 Data. 309 9.6.2 Specification Tests. 309 9.6.3 Gradient Estimates. 310 9.7 Conclusion. 311 Acknowledgements. 312 Appendix. 313 Assumptions. 313 Asymptotic Results for the Within Estimator. 313 Lemmas with Corresponding Proofs .316
References. 321 10 Multi-dimensional Panels in Quantile Regression Models. 325 Antonio F. Galvao and Gabriel V. Montes-Rojas 10.1 Introduction. 325 10.2 Fixed Effects Models. 327 10.2.1 Estimation and Implementation. 330 10.2.2 Inference Procedures. 331 10.2.3 Smoothed Quantile Regression Panel Data . 335 10.3 Random Effects Models.340 10.3.1 Model. 340 10.3.2 Estimation and Implementation. 343 10.3.3 Inference Procedures. 343 10.3.4 Correlated Random Effects Models. 344
xvi Contents 10.4 Quantile Models for Network Data. 346 10.5 Specific Guidelines for Practitioners. 347 References. 348 11 Multi-Dimensional Models for Spatial Panels. 353 Julie Le Gallo and Alain Pirotte 11.1 Introduction. 353 11.2 Spatial Models . 355 11.2.1 The Baseline Model .355 11.2.2 Unobserved Heterogeneity. 359 11.3 Spatial Estimation Methods. 361 11.3.1 Maximum Likelihood Estimation. 361 11.3.2 GM, FGLS and Instrumental Variables Approaches. 363 11.4 Testing for Spatial Dependence. 369 11.5 Prediction with Spatial Models.371 11.6 Some Further Topics. 372 11.6.1 Heterogenous Coefficients Spatial Models. 372 11.6.2 Time-Space Models . 374 11.7
Conclusion. 375 References. 375 12 The Econometrics of Gravity Models in International Trade. 381 Badi H. Baltagi, Peter H. Egger and Katharina Erhardt 12.1 Introduction. 381 12.2 Generic Theoretical Background . 382 12.3 Specific Problems with Estimating Gravity Models. 386 12.3.1 Heteroskedasticity. 387 12.3.2 Modelling the Mass Point at Zero Bilateral Trade Flows .388 12.3.3 Dynamics. 391 12.3.4 Spatial Data: Interdependence of Bilateral Trade Flows Conditional on Exogenous Determinants . 391 12.3.5 Endogenous Regressors. 394 12.3.6 Ratio Estimators . 399 12.3.7 Binary Regressors and Event-study Designs. 401 12.3.8 High-dimensional Data Designs .402 12.3.9 Nonparametric Analysis and Machine Learning. 403 12.3.10 Inference. 404 12.4
Conclusion.405 References. 405 13 Modelling Housing Using Multi-dimensional Panel Data. 413 Badi H. Baltagi and Georges Bresson 13.1 Introduction. 414 13.2 Discrete Choice Models and Hedonic Price Functions: A Quick Overview. 415
Contents xvii Multi-dimensional Models of Housing Hedonic Price Functions: Some Examples. 417 13.4 Multi-dimensional Models of Residential Mobility and Location Choice: Some Examples . 424 13.5 Multi-dimensional Dynamic Models of Housing Models . 432 13.6 From Variational Inference to Multi-dimensional Housing Models 437 13.7 Conclusion. 444 Appendix: Inverse G-Wishart distributions. 447 References. 447 13.3 14 Modelling Migration. 455 Raul Ramos 14.1 Introduction and Objectives. 455 14.2 Micro-foundations of the Gravity Model of Migration. 456 14.3 Data Limitations and Estimation Issues. 458 14.3.1 Data and Measurement Issues.458 14.3.2 Missing and Incomplete Data.460 14.3.3 Logs and Zeros . 462 14.3.4 Multilateral Resistance to Migration. 463 14.3.5
Endogeneity. 465 14.3.6 Spatial Models. 467 14.4 Concluding Remarks. 471 Acknowledgements. 472 References. 473 15 Multi-dimensional Panels in Health Economics with an Application on Antibiotic Consumption. 479 Aniko Biro, Péter Elek, and Nora Kungl 15.1 Introduction. 479 15.2 Literature. 480 15.2.1 Explaining Variation in Healthcare Use with Multi-dimensional Fixed Effects Models. 481 15.2.2 Data Limitations and Empirical Issues. 489 15.3 Application on Antibiotic Consumption. 491 15.3.1 Literature.491 15.3.2 Data. 492 15.3.3 Decomposing Antibiotic Consumption into Place- and Patient-specific Components. 495 15.3.4
Effect of Primary Care Availability on Antibiotic Use. 500 15.4 Conclusions. 504 Acknowledgements.504 References. 504
xviii 16 Contents Can Machine Learning Beat Gravity in Flow Prediction?. 511 György Ruzicska, Ramzi Chariag, Olivér Kiss, and Miklos Koren 16.1 Introduction . 512 16.2 Gravity Models for FlowPrediction. 514 16.3 Machine Learning Models for Flow Prediction. 517 16.3.1 Random Forest Model. 518 16.3.2 Neural Networks. 523 16.3.3 Graph Neural Network. 527 16.4 Empirical Evaluation of the Models. 531 16.4.1 Data Description and Characteristics . 532 16.4.2 Evaluation Metrics. 535 16.4.3 Experimental Results . 536 16.4.4 Discussion. 541 16.5 Concluding Remarks . 542 References. 543 Index. 547 |
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| spelling | The econometrics of multi-dimensional panels theory and applications Laszlo Matyas, editor ; foreword by Marc Nerlove Second edition Cham Springer [2024] ©2024 xxi, 551 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Advanced Studies in Theoretical and Applied Econometrics Volume 54 Econometrics Quantitative Economics Game Theory Statistics in Business, Management, Economics, Finance, Insurance Mathematics in Business, Economics and Finance Data Analysis and Big Data Game theory Statistics Social sciences / Mathematics Quantitative research (DE-588)4143413-4 Aufsatzsammlung gnd-content Mátyás, László 1957- (DE-588)131764632 edt Nerlove, Marc L. 1933- (DE-588)170066894 aui Erscheint auch als Online-Ausgabe 978-3-031-49849-7 Advanced Studies in Theoretical and Applied Econometrics Volume 54 (DE-604)BV000002376 54 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035153204&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | The econometrics of multi-dimensional panels theory and applications Advanced Studies in Theoretical and Applied Econometrics Econometrics Quantitative Economics Game Theory Statistics in Business, Management, Economics, Finance, Insurance Mathematics in Business, Economics and Finance Data Analysis and Big Data Game theory Statistics Social sciences / Mathematics Quantitative research |
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| title | The econometrics of multi-dimensional panels theory and applications |
| title_auth | The econometrics of multi-dimensional panels theory and applications |
| title_exact_search | The econometrics of multi-dimensional panels theory and applications |
| title_full | The econometrics of multi-dimensional panels theory and applications Laszlo Matyas, editor ; foreword by Marc Nerlove |
| title_fullStr | The econometrics of multi-dimensional panels theory and applications Laszlo Matyas, editor ; foreword by Marc Nerlove |
| title_full_unstemmed | The econometrics of multi-dimensional panels theory and applications Laszlo Matyas, editor ; foreword by Marc Nerlove |
| title_short | The econometrics of multi-dimensional panels |
| title_sort | the econometrics of multi dimensional panels theory and applications |
| title_sub | theory and applications |
| topic | Econometrics Quantitative Economics Game Theory Statistics in Business, Management, Economics, Finance, Insurance Mathematics in Business, Economics and Finance Data Analysis and Big Data Game theory Statistics Social sciences / Mathematics Quantitative research |
| topic_facet | Econometrics Quantitative Economics Game Theory Statistics in Business, Management, Economics, Finance, Insurance Mathematics in Business, Economics and Finance Data Analysis and Big Data Game theory Statistics Social sciences / Mathematics Quantitative research Aufsatzsammlung |
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