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E-raamat: Spatial Econometrics using Microdata [Wiley Online]

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  • Formaat: 256 pages
  • Sari: ISTE
  • Ilmumisaeg: 31-Oct-2014
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119008654
  • ISBN-13: 9781119008651
Teised raamatud teemal:
  • Wiley Online
  • Hind: 174,45 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Spatial Econometrics using MicrodataSuurem pilt
  • Formaat: 256 pages
  • Sari: ISTE
  • Ilmumisaeg: 31-Oct-2014
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119008654
  • ISBN-13: 9781119008651
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119008651
This book provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data.

Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency.

The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares approach.

This book is a popularized reference for students looking to work with spatialized data, but who do not have the advanced statistical theoretical basics.
Acknowledgments ix
Preface xi
Chapter 1 Econometrics And Spatial Dimensions 1(28)
1.1 Introduction
1(5)
1.2 The types of data
6(5)
1.2.1 Cross-sectional data
7(1)
1.2.2 Time series
8(1)
1.2.3 Spatio-temporal data
9(2)
1.3 Spatial econometrics
11(5)
1.3.1 A picture is worth a thousand words
13(2)
1.3.2 The structure of the databases of spatial microdata
15(1)
1.4 History of spatial econometrics
16(5)
1.5 Conclusion
21(8)
Chapter 2 Structuring Spatial Relations 29(30)
2.1 Introduction
29(1)
2.2 The spatial representation of data
30(4)
2.3 The distance matrix
34(3)
2.4 Spatial weights matrices
37(13)
2.4.1 Connectivity relations
40(2)
2.4.2 Relations of inverse distance
42(3)
2.4.3 Relations based on the inverse (or negative) exponential
45(2)
2.4.4 Relations based on Gaussian transformation
47(1)
2.4.5 The other spatial relation
47(1)
2.4.6 One choice in particular?
48(1)
2.4.7 To start
49(1)
2.5 Standardization of the spatial weights matrix
50(1)
2.6 Some examples
51(4)
2.7 Advantages/disadvantages of micro-data
55(1)
2.8 Conclusion
56(3)
Chapter 3 Spatial Autocorrelation 59(34)
3.1 Introduction
59(6)
3.2 Statistics of global spatial autocorrelation
65(12)
3.2.1 Moran's I statistic
68(4)
3.2.2 Another way of testing significance
72(2)
3.2.3 Advantages of Moran's I statistic in modeling
74(1)
3.2.4 Moran's I for determining the optimal form of W
75(2)
3.3 Local spatial autocorrelation
77(9)
3.3.1 The LISA indices
79(7)
3.4 Some numerical examples of the detection tests
86(3)
3.5 Conclusion
89(4)
Chapter 4 Spatial Econometric Models 93(52)
4.1 Introduction
93(2)
4.2 Linear regression models
95(7)
4.2.1 The different multiple linear regression model types
99(3)
4.3 Link between spatial and temporal models
102(13)
4.3.1 Temporal autoregressive models
103(7)
4.3.2 Spatial autoregressive models
110(5)
4.4 Spatial autocorrelation sources
115(14)
4.4.1 Spatial externalities
117(2)
4.4.2 Spillover effect
119(4)
4.4.3 Omission of variables or spatial heterogeneity
123(4)
4.4.4 Mixed effects
127(2)
4.5 Statistical tests
129(11)
4.5.1 LM tests in spatial econometrics
134(6)
4.6 Conclusion
140(5)
Chapter 5 Spatio-Temporal Modeling 145(32)
5.1 Introduction
145(3)
5.2 The impact of the two dimensions on the structure of the links: structuring of spatio-temporal links
148(2)
5.3 Spatial representation of spatio-temporal data
150(4)
5.4 Graphic representation of the spatial data generating processes pooled over time
154(5)
5.5 Impacts on the shape of the weights matrix
159(3)
5.6 The structuring of temporal links: a temporal weights matrix
162(5)
5.7 Creation of spatio-temporal weights matrices
167(3)
5.8 Applications of autocorrelation tests and of autoregressive models
170(2)
5.9 Some spatio-temporal applications
172(1)
5.10 Conclusion
173(4)
Conclusion 177(8)
Glossary 185(4)
Appendix 189(26)
Bibliography 215(12)
Index 227
Jean DUBÉ is Professor in regional development at Laval University, Canada.

Diègo LEGROS is a lecturer in economics and management at the University of Burgundy, France.
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