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E-raamat: Spatial Microeconometrics [Taylor & Francis e-raamat]

, (University of Trento, Italy), (University of Trento, Italy)
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Spatial MicroeconometricsSuurem pilt
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Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781315735276
Spatial Microeconometrics introduces the reader to the basic concepts of spatial statistics, spatial econometrics and the spatial behavior of economic agents at the microeconomic level. Incorporating useful examples and presenting real data and datasets on real firms, the book takes the reader through the key topics in a systematic way.

The book outlines the specificities of data that represent a set of interacting individuals with respect to traditional econometrics that treat their locational choices as exogenous and their economic behavior as independent. In particular, the authors address the consequences of neglecting such important sources of information on statistical inference and how to improve the model predictive performances. The book presents the theory, clarifies the concepts and instructs the readers on how to perform their own analyses, describing in detail the codes which are necessary when using the statistical language R.

The book is written by leading figures in the field and is completely up to date with the very latest research. It will be invaluable for graduate students and researchers in economic geography, regional science, spatial econometrics, spatial statistics and urban economics.
Foreword xiii
Lung-Fei Lee
Preface and acknowledgements xix
PART I Introduction
1(12)
1 Foundations of spatial microeconometrics modeling
3(10)
1.1 A micro-level approach to spatial econometrics
3(2)
1.2 Advantages of spatial microeconometric analysis
5(2)
1.3 Sources of spatial micro-data
7(1)
1.4 Sources of uncertainty in spatial micro-data
8(2)
1.5 Conclusions and plan of the book
10(3)
PART II Modeling the spatial behavior of economic agents in a given set of locations
13(78)
2 Preliminary definitions and concepts
15(15)
2.1 Neighborhood and the W matrix
15(7)
2.2 Moran's I and other spatial correlation measures
22(4)
2.3 The Moran scatterplot and local indicators of spatial correlation
26(3)
2.4 Conclusions
29(1)
3 Basic cross-sectional spatial linear models
30(27)
3.1 Introduction
30(1)
3.2 Regression models with spatial autoregressive components
30(14)
3.2.1 Pure spatial autoregression
30(2)
3.2.2 The spatial error model
32(3)
3.2.3 The spatial lag model
35(4)
3.2.4 The spatial Durbin model
39(2)
3.2.5 The general spatial autoregressive model with spatial autoregressive error structure
41(3)
3.3 Test of residual spatial autocorrelation with explicit alternative hypotheses
44(2)
3.4 Marginal impacts
46(2)
3.5 Effects of spatial imperfections of micro-data
48(5)
3.5.1 Introduction
48(1)
3.5.2 Measurement error in spatial error models
49(1)
3.5.3 Measurement error in spatial lag models
50(3)
3.6 Problems in regressions on a spatial distance
53(4)
4 Non-linear spatial models
57(19)
4.1 Non-linear spatial regressions
57(1)
4.2 Standard non-linear models
58(5)
4.2.1 Logit and probit models
58(2)
4.2.2 The tobit model
60(3)
4.3 Spatial probit and logit models
63(9)
4.3.1 Model specification
63(2)
4.3.2 Estimation
65(7)
4.4 The spatial tobit model
72(1)
4.4.1 Model specification
72(1)
4.4.2 Estimation
72(1)
4.5 Further non-linear spatial models
73(1)
4.6 Marginal impacts in spatial non-linear models
74(2)
5 Space-time models
76(15)
5.1 Generalities
76(1)
5.2 Fixed and random effects models
76(1)
5.3 Random effects spatial models
77(2)
5.4 Fixed effect spatial models
79(1)
5.5 Estimation
80(5)
5.5.1 Introduction
80(1)
5.5.2 Maximum likelihood
80(1)
5.5.2.1 Likelihood procedures for random effect models
80(2)
5.5.2.2 Likelihood procedures for fixed effect models
82(1)
5.5.3 The generalized method of moments approach
83(1)
5.5.3.1 Generalized method of moments procedures for random effects models
84(1)
5.5.3.2 Generalized method of moments procedures for fixed effects models
85(1)
5.6 A glance at further approaches in spatial panel data modeling
85(6)
PART III Modeling the spatial locational choices of economic agents
91(66)
6 Preliminary definitions and concepts in point pattern analysis
93(20)
6.1 Spatial point patterns of economic agents
93(1)
6.2 The hypothesis of complete spatial randomness
94(1)
6.3 Spatial point processes
95(12)
6.3.1 Homogeneous Poisson point process
96(2)
6.3.2 Aggregated point processes
98(1)
6.3.2.1 Inhomogeneous Poisson point processes
98(2)
6.3.2.2 Cox processes
100(1)
6.3.2.3 Poisson cluster point processes
101(3)
6.3.3 Regular point processes
104(3)
6.4 Classic exploratory tools and summary statistics for spatial point patterns
107(6)
6.4.1 Quadrat-based methods
107(3)
6.4.2 Distance-based methods
110(3)
7 Models of the spatial location of individuals
113(14)
7.1 Ripley's K-function
113(1)
7.2 Estimation of Ripley's k-function
114(2)
7.3 Identification of spatial location patterns
116(11)
7.3.1 The CSR test
116(5)
7.3.2 Parameter estimation of the Thomas cluster process
121(3)
7.3.3 Parameter estimation of the Matern cluster process
124(2)
7.3.4 Parameter estimation of the log-Gaussian Cox process
126(1)
8 Points in a heterogeneous space
127(16)
8.1 Diggle and Chetwynd's D-function
127(5)
8.2 Baddeley, Meller and Waagepetersen's kinhom-function
132(6)
8.2.1 Estimation of Kinhom-function
133(2)
8.2.2 Inference for Kinhom-function
135(3)
8.3 Measuring spatial concentration of industries: Duranton-Overman K-density and Marcon-Puech M-function
138(5)
8.3.1 Duranton and Overman's k-density
139(1)
8.3.2 Marcon and Puech's M-function
140(3)
9 Space-time models
143(14)
9.1 Diggle, Chetwynd, Hdggkvist and Morris' space-time K-function
143(5)
9.1.1 Estimation of space-time K1 function
145(1)
9.1.2 Detecting space-time clustering of economic events
145(3)
9.2 Gabriel and Diggle's STIK-function
148(9)
9.2.1 Estimation of STTX-function and inference
152(5)
PART IV Looking ahead: modeling both the spatial location choices and the spatial behavior of economic agents
157(30)
10 Firm demography and survival analysis
159(28)
10.1 Introduction
159(2)
10.2 A spatial microeconometric model for firm demography
161(11)
10.2.1 A spatial model for firm demography
161(1)
10.2.1.1 Introduction
161(1)
10.2.1.2 The birth model
162(1)
10.2.1.3 The growth model
163(1)
10.2.1.4 The survival model
163(1)
10.2.2 A case study
164(1)
10.2.2.1 Data description
164(2)
10.2.2.2 The birth model
166(3)
10.2.2.3 The growth model
169(1)
10.2.2.4 The survival model
170(2)
10.2.3 Conclusions
172(1)
10.3 A spatial microeconometric model for firm survival
172(14)
10.3.1 Introduction
172(1)
10.3.2 Basic survival analysis techniques
173(3)
10.3.3 Case study: The survival of pharmaceutical and medical device manufacturing start-up firms in Italy
176(1)
10.3.3.1 Data description
176(1)
10.3.3.2 Definition of the spatial microeconometric covariates
177(3)
10.3.3.3 Definition of the control variables
180(1)
10.3.3.4 Empirical results
181(5)
10.4 Conclusion
186(1)
Appendices
Appendix 1 Some publicly available spatial datasets
187(1)
Appendix 2 Creation of aW matrix and preliminary computations
188(3)
Appendix 3 Spatial linear models
191(1)
Appendix 4 Non-linear spatial models
192(1)
Appendix 5 Space-time models
193(1)
Appendix 6 Preliminary definitions and concepts in point pattern analysis
194(5)
Appendix 6.1 Point pattern datasets
194(1)
Appendix 6.2 Simulating point patterns
195(1)
Appendix 6.2.1 Homogeneous Poisson processes
195(1)
Appendix 6.2.2 Inhomogeneous Poisson processes
196(1)
Appendix 6.2.3 Cox processes
196(1)
Appendix 6.2.4 Poisson cluster processes
196(1)
Appendix 6.2.5 Regular processes
197(1)
Appendix 6.3 Quadrat-based analysis
197(1)
Appendix 6.4 Clark-Evans test
198(1)
Appendix 7 Models of the spatial location of individuals
199(1)
Appendix 7.1 AT-function-based CSRtest
199(1)
Appendix 7.2 Point process parameters estimation by the method of minimum contrast
199(1)
Appendix 8 Points in a heterogeneous space
200(4)
Appendix 8.1 D-function-based test of spatial interactions
200(2)
Appendix 8.2 Kinhom-function-based test of spatial interactions
202(1)
Appendix 8.3 Duranton-Overman AT-density and Marcon-Puech M-function
203(1)
Appendix 9 Space-time models
204(3)
Appendix 9.1 Space-time AT-function
204(1)
Appendix 9.2 Gabriel and Diggle's STIK-function
205(2)
Bibliography 207(12)
Index 219
Giuseppe Arbia is full professor of economic statistics at the Faculty of Economics, Catholic University of the Sacred Heart, Rome, and lecturer at the University of Italian Switzerland in Lugano. Since 2006 he has been president of the Spatial Econometrics Association and since 2008 he has chaired the Spatial Econometrics Advanced Institute. He is also a member of many international scientific societies.

Giuseppe Espa is full professor in economic statistics at the Department of Economics and Management of the University of Trento and the LUISS "Guido Carli" University of Rome.

Diego Giuliani is associate professor in economic statistics at the Department of Economics and Management of the University of Trento. He works primarily on the use and development of statistical methods to analyze firm-level micro-geographic data.
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