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Econometrics of Multi-dimensional Panels: Theory and Applications 1st ed. 2017 [Kõva köide]

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Econometrics of Multi-dimensional Panels: Theory and Applications 1st ed. 2017Suurem pilt
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This book presents the econometric foundations and applications of multi-dimensional panels, including modern methods of big data analysis.The last two decades or so, the use of panel data has become a standard in many areas of economic analysis. The available models formulations became more complex, the estimation and hypothesis testing methods more sophisticated. The interaction between economics and econometrics resulted in a huge publication output, deepening and widening immensely our knowledge and understanding in both. The traditional panel data, by nature, are two-dimensional. Lately, however, as part of the big data revolution, there has been a rapid emergence of three, four and even higher dimensional panel data sets. These have started to be used to study the flow of goods, capital, and services, but also some other economic phenomena that can be better understood in higher dimensions. Oddly, applications rushed ahead of theory in this field.This book is aimed at fi

lling this widening gap. The first theoretical part of the volume is providing the econometric foundations to deal with these new high-dimensional panel data sets. It not only synthesizes our current knowledge, but mostly, presents new research results. The second empirical part of the book provides insight into the most relevant applications in this area. These chapters are a mixture of surveys and new results, always focusing on the econometric problems and feasible solutions.

Fixed Effects Models.- Random Effects Models.- Models with Endogenous Regressors.- Dynamic Models and Reciprocity.- Random Coefficients Models.- Discrete Response Models.- Nonparametric Models with Random Effects.- Multi-dimensional Panels in Quantile Regression Models.- Models for Spatial Panels.- Modelling in the Presence of Cross-sectional Error Dependence.- The Estimation of Gravity Models in International Trade.- Modelling Housing Using Multi-dimensional Panel Data.- Modelling Migration.- Modeling Heterogeneity in Country-Industry-Year Panel Data: Two Illustrative Econometric Analyses.- The Determinants of Consumer Price Dispersion: Evidence from French Supermarkets.
Preface vii
1 Fixed Effects Models
1(34)
Laszlo Balazsi
Laszlo Matyas
Tom Wansbeek
1.1 Introduction
1(4)
1.2 Models with Different Types of Heterogeneity
5(3)
1.3 Least Squares Estimation of the Models
8(4)
1.4 Incomplete Panels
12(1)
1.5 The Within Estimator
13(6)
1.5.1 The Equivalence of the LSDV and the Within Estimator
13(2)
1.5.2 Incomplete Panels and the Within Estimator
15(4)
1.6 Heteroscedasticity and Cross-correlation
19(5)
1.6.1 The New Covariance Matrices and the GLS Estimator
20(1)
1.6.2 Estimation of the Variance Components and the Cross Correlations
21(3)
1.7 Extensions to Higher Dimensions
24(3)
1.7.1 Different Forms of Heterogeneity
24(1)
1.7.2 Least Squares and the Within Estimators
25(1)
1.7.3 Incomplete Panels
25(2)
1.8 Varying Coefficients Models
27(3)
References
30(5)
2 Random Effects Models
35(36)
Laszlo Balazsi
Badi H. Baltagi
Laszlo Matyas
Daria Pus
2.1 Introduction
35(1)
2.2 Different Model Specifications
36(8)
2.2.1 Various Heterogeneity Formulations
37(2)
2.2.2 Spectral Decomposition of the Covariance Matrices
39(5)
2.3 FGLS Estimation
44(5)
2.4 Unbalanced Data
49(8)
2.4.1 Structure of the Covariance Matrices
49(3)
2.4.2 The Inverse of the Covariance Matrices
52(2)
2.4.3 Estimation of the Variance Components
54(3)
2.5 Extensions
57(3)
2.5.1 4D and Beyond
57(1)
2.5.2 Mixed FE-RE Models
58(2)
2.6 Testing
60(2)
2.7 Conclusion
62(1)
References
62(3)
Appendix 1
65(1)
Example for normalizing with 1: Model (2.14), T → ∞
65(1)
Example for normalizing with √N1N2/A: Model (2.2), N1, N2 → ∞
66(1)
Appendix 2 Proof of formula (2.19)
66(1)
Appendix 3 Inverse of (2.34), and the estimation of the variance components
67(4)
3 Models with Endogenous Regressors
71(30)
Laszlo Balazsi
Maurice J.G. Bun
Felix Chan
Mark N. Harris
3.1 Introduction
72(2)
3.2 The Hausman-Taylor-like Instrument Variable Estimator
74(13)
3.2.1 A Simple Approach
74(1)
3.2.2 Sources of Endogeneity
75(1)
3.2.3 The Hausman-Taylor Estimator
76(4)
3.2.4 Time Varying Individual Specific Effects
80(5)
3.2.5 Properties
85(1)
3.2.6 Using External Instruments
86(1)
3.3 The Non-linear Generalized Method of Moments Estimator
87(1)
3.4 Mixed Effects Models
88(2)
3.5 Exogeneity Tests
90(4)
3.5.1 Testing for Endogeneity
90(1)
3.5.2 Testing for Instrument Validity
91(1)
3.5.3 Testing in the Case of Fixed Effects
92(2)
3.6 Further Considerations
94(2)
3.6.1 Incomplete Data
94(1)
3.6.2 Notes on Higher-dimensional Panels
95(1)
References
96(1)
Appendix
97(4)
4 Dynamic Models and Reciprocity
101(24)
Maurice J.G. Bun
Felix Chan
Mark N. Harris
4.1 Introduction
101(2)
4.2 Dynamics
103(6)
4.2.1 Estimation
104(3)
4.2.2 Monte Carlo Experiments
107(2)
4.3 Reciprocity
109(5)
4.3.1 Within Estimator
110(2)
4.3.2 GMM Estimation
112(1)
4.3.3 No Self-flow
113(1)
4.4 Combining Dynamics and Reciprocity
114(3)
4.4.1 Monte Carlo Experiments
116(1)
4.5 Extensions
117(2)
4.5.1 Generalized Reciprocity
117(1)
4.5.2 Higher Dimensions
118(1)
References
119(2)
Appendix
121(4)
5 Random Coefficients Models
125(38)
Jaya Krishnakumar
Monika Avila Marquez
Laszlo Balazsi
5.1 Introduction
125(3)
5.2 The Linear Model for Three Dimensions
128(9)
5.2.1 The Model
128(1)
5.2.2 Feasible Generalized Least Squares (FGLS)
129(1)
5.2.3 Method 1: Using Within Dimensions Variation
130(3)
5.2.4 Method 2: Using the Overall Variation
133(2)
5.2.5 Minimum Norm Quadratic Unbiased Estimation (MINQUE)
135(1)
5.2.6 Properties of the Estimators
136(1)
5.3 Maximum Likelihood Estimation
137(2)
5.3.1 The Unrestricted Maximum Likelihood
137(1)
5.3.2 Restricted Maximum Likelihood Estimation
138(1)
5.4 Inference: Varying (Random) or Constant Coefficients?
139(6)
5.4.1 Testing for Methods 1 and 2
139(4)
5.4.2 Testing in the Case of MLE
143(2)
5.5 Prediction of the Coefficients
145(1)
5.6 Bayesian Approach
146(1)
5.7 Extensions within the Linear Model
147(8)
5.7.1 Alternative Model Formulations
147(2)
5.7.2 Incomplete Panels
149(1)
5.7.3 Cross-Sectional Dependence
150(2)
5.7.4 Random Coefficients Correlated with the Explanatory Variables
152(1)
5.7.5 Some Random and Some "Fixed" Coefficients?
153(1)
5.7.6 Higher Dimensions
154(1)
5.8 Non-linear Extension: RC Probit Model
155(1)
5.9 A Simulation Experiment
156(2)
5.10 Conclusions
158(1)
References
159(4)
6 Discrete Response Models
163(32)
Balazs Kertesz
6.1 Introduction
163(1)
6.2 Fixed Effects Binary Choice Models
164(14)
6.2.1 Model Assumptions
165(1)
6.2.2 Problems with Non-linear Fixed Effects Models
166(3)
6.2.3 Elimination of Fixed Effects
169(7)
6.2.4 Caveats of the Procedure
176(2)
6.2.5 Unbalanced Panels
178(1)
6.3 Selection Bias
178(6)
6.3.1 Parametric Approach
180(1)
6.3.2 Semi-Parametric Approach
181(2)
6.3.3 Non-Parametric Approach
183(1)
6.4 Fixed Effects Multinomial Choice Models
184(2)
References
186(3)
Appendix
189(6)
7 Nonparametric Models with Random Effects
195(44)
Yiguo Sun
Wei Lin
Qi Li
7.1 Introduction
195(2)
7.2 The Pooled Local Linear Estimator
197(7)
7.3 Two-step Local Linear Estimator
204(9)
7.3.1 Weighted Local Linear Estimator
205(2)
7.3.2 Two-step Estimator
207(6)
7.4 Pairwise Random Effects
213(17)
7.4.1 Cases (i)--(iii): The Sample Size Increases in One Index Only
215(3)
7.4.2 Cases (iv)-(vi): The Sample Size Increases in Two out of the Three Indices
218(5)
7.4.3 Case (vii): The Sample Size Increases in All Three Indices
223(7)
7.5 Some Extensions
230(4)
7.5.1 Mixed Fixed and Random Effects Models
230(2)
7.5.2 Four and Higher-dimensional Cases
232(1)
7.5.3 Fixed Effects Models
233(1)
7.6 Conclusion
234(1)
References
235(1)
Appendix
236(3)
8 Multi-dimensional Panels in Quantile Regression Models
239(24)
Antonio F. Galvao
Gabriel V. Montes-Rojas
8.1 Introduction
239(2)
8.2 Fixed Effects Models
241(11)
8.2.1 Estimation and Implementation
244(1)
8.2.2 Inference Procedures
245(3)
8.2.3 Smoothed Quantile Regression Panel Data
248(4)
8.3 Random Effects Models
252(5)
8.3.1 Model
253(2)
8.3.2 Estimation and Implementation
255(1)
8.3.3 Inference Procedures
256(1)
8.4 Correlated Random Effects Models
257(1)
8.5 Specific Guidelines for Practitioners
258(1)
References
259(4)
9 Models for Spatial Panels
263(28)
Julie Le Gallo
Alain Pirotte
9.1 Introduction
263(2)
9.2 Spatial Models
265(6)
9.2.1 The Baseline Model
265(3)
9.2.2 Unobserved Heterogeneity
268(3)
9.3 Spatial Estimation Methods
271(8)
9.3.1 Maximum Likelihood Estimation
271(2)
9.3.2 GMM, FGLS and Instrumental Variables Approaches
273(6)
9.4 Testing for Spatial Dependence
279(1)
9.5 Prediction with Spatial Models
280(2)
9.6 Some Further Topics
282(2)
9.6.1 Heterogenous Coefficients Spatial Models
282(2)
9.6.2 Time-Space Models
284(1)
9.7 Conclusion
284(1)
References
285(6)
10 Modelling in the Presence of Cross-sectional Error Dependence
291(32)
George Kapetanios
Camilla Mastromarco
Laura Serlenga
Yongcheol Shin
10.1 Introduction
291(3)
10.2 3D Models with Cross-sectional Error Dependence
294(5)
10.3 Cross-sectional Dependence (CD) Test
299(5)
10.4 Extensions
304(7)
10.4.1 Unbalanced Panels
304(5)
10.4.2 4D Model Extensions
309(2)
10.5 Monte Carlo Analysis
311(1)
10.6 Empirical Application to the Gravity Model of the Intra-EU Trade
312(7)
10.7 Conclusion
319(1)
References
319(4)
11 The Estimation of Gravity Models in International Trade
323(26)
Badi H. Baltagi
Peter H. Egger
Katharina Erhardt
11.1 Introduction
323(1)
11.2 Generic Theoretical Background
324(4)
11.3 Specific Problems with Estimating Gravity Models
328(15)
11.3.1 Heteroskedasticity
329(1)
11.3.2 Modelling the Mass Point at Zero Bilateral Trade Flows
330(2)
11.3.3 Dynamics
332(1)
11.3.4 Spatial Data: Interdependence of Bilateral Trade Flows Conditional on Exogenous Determinants
333(3)
11.3.5 Endogenous Regressors
336(5)
11.3.6 Ratio Estimators
341(2)
11.4 Conclusion
343(1)
References
343(6)
12 Modelling Housing Using Multi-dimensional Panel Data
349(28)
Badi H. Baltagi
Georges Bresson
12.1 Introduction
349(1)
12.2 Discrete Choice Models and Hedonic Price Functions: A Quick Overview
350(3)
12.3 Multi-dimensional Models of Housing Hedonic Price Functions: Some Examples
353(7)
12.4 Multi-dimensional Models of Residential Mobility and Location Choice: Some Examples
360(6)
12.5 Multi-dimensional Dynamic Models of Housing Models
366(5)
12.6 Conclusion
371(1)
References
372(5)
13 Modelling Migration
377(20)
Raul Ramos
13.1 Introduction and Objectives
377(1)
13.2 Micro-foundations of the Gravity Model of Migration
378(2)
13.3 Data Limitations and Estimation Issues
380(11)
13.3.1 Data and Measurement Issues
380(1)
13.3.2 Missing and Incomplete Data
381(1)
13.3.3 Logs and Zeros
382(2)
13.3.4 Multilateral Resistance to Migration
384(2)
13.3.5 Endogeneity
386(1)
13.3.6 Spatial Models
387(4)
13.4 Concluding Remarks
391(2)
References
393(4)
14 Modeling Heterogeneity in Country-Industry-Year Panel Data: Two Illustrative Econometric Analyses
397(30)
Jimmy Lopez
Jacques Mairesse
14.1 Introduction
397(3)
14.2 ICT, R&D, and Productivity
400(9)
14.2.1 Literature Review
400(1)
14.2.2 Model and Data
401(1)
14.2.3 Discussion of the Dynamic Specification
402(1)
14.2.4 Three-dimensional Structure and Fixed Effects
403(3)
14.2.5 Heterogeneity of Factor Effects
406(2)
14.2.6 Discussion of Cointegration
408(1)
14.3 Productivity Impact of Non-Manufacturing Regulations
409(7)
14.3.1 Literature Review
409(1)
14.3.2 Model and Data
410(1)
14.3.3 Estimation Strategy with 2D Explanatory Variables
411(2)
14.3.4 Estimation Results
413(2)
14.3.5 Discussion of Heterogeneous Effects with 2D Explanatory Variables
415(1)
14.4 Conclusion
416(1)
References
417(2)
Appendix
419(8)
Appendix 1 Data
419(3)
Appendix 2 Supplementary Estimation Tables
422(5)
15 The Determinants of Consumer Price Dispersion: Evidence from French Supermarkets
427(24)
Nicoletta Berardi
Patrick Sevestre
Jonathan Thebault
15.1 Introduction
427(2)
15.2 Data Description
429(4)
15.2.1 Grocery Price Data
429(2)
15.2.2 Supermarket Data and Competition Measures
431(2)
15.3 Assessing Price Dispersion in the French Retail Sector
433(4)
15.4 Disentangling the Sources of Price Dispersion
437(8)
15.5 Conclusion
445(1)
References
446(2)
Appendix
448(3)
Index 451
Laszlo Matyas is a well-known Hungarian-Australian economist/econometrician. He (co)authored and (co)edited several high impact publications in econometrics, mostly in the field of panel data. Currently he is a University Professor at the Central European University (CEU Budapest, Hungary). Earlier, among others, worked as Senior Lecturer at Monash University (Melbourne, Australia), was the founding Director of the Institute for Economic Analysis (Budapest, Hungary), and also served as Provost of CEU. The new volume he put together on the Econometrics of Multi-dimensional Panels, forthcoming with Springer-Verlag in 2017, is the 10th book he compiled over the last two decades.