Spatial Microeconometrics

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Table of Contents
Foreword
Preface and acknowledgements
PART I: Introduction
	1 Foundations of spatial microeconometrics modeling
		1.1 A micro-level approach to spatial econometrics
		1.2 Advantages of spatial microeconometric analysis
		1.3 Sources of spatial micro-data
		1.4 Sources of uncertainty in spatial micro-data
		1.5 Conclusions and plan of the book
PART II: Modeling the spatial behavior of economic agents in a given set of locations
	2 Preliminary definitions and concepts
		2.1 Neighborhood and the W matrix
		2.2 Moran’s I and other spatial correlation measures
		2.3 The Moran scatterplot and local indicators of spatial correlation
		2.4 Conclusions
	3 Basic cross-sectional spatial linear models
		3.1 Introduction
		3.2 Regression models with spatial autoregressive components
			3.2.1 Pure spatial autoregression
			3.2.2 The spatial error model
			3.2.3 The spatial lag model
			3.2.4 The spatial Durbin model
			3.2.5 The general spatial autoregressive model with spatial autoregressive error structure
		3.3 Test of residual spatial autocorrelation with explicit alternative hypotheses
		3.4 Marginal impacts
		3.5 Effects of spatial imperfections of micro-data
			3.5.1 Introduction
			3.5.2 Measurement error in spatial error models
			3.5.3 Measurement error in spatial lag models
		3.6 Problems in regressions on a spatial distance
	4 Non-linear spatial models
		4.1 Non-linear spatial regressions
		4.2 Standard non-linear models
			4.2.1 Logit and probit models
			4.2.2 The tobit model
		4.3 Spatial probit and logit models
			4.3.1 Model specification
			4.3.2 Estimation
		4.4 The spatial tobit model
			4.4.1 Model specification
			4.4.2 Estimation
		4.5 Further non-linear spatial models
		4.6 Marginal impacts in spatial non-linear models
	5 Space–time models
		5.1 Generalities
		5.2 Fixed and random effects models
		5.3 Random effects spatial models
		5.4 Fixed effect spatial models
		5.5 Estimation
			5.5.1 Introduction
			5.5.2 Maximum likelihood
				5.5.2.1 Likelihood procedures for random effect models
				5.5.2.2 Likelihood procedures for fixed effect models
			5.5.3 The generalized method of moments approach
				5.5.3.1 Generalized method of moments procedures for random effects models
				5.5.3.2 Generalized method of moments procedures for fixed effects models
		5.6 A glance at further approaches in spatial panel data modeling
PART III: Modeling the spatial locational choices of economic agents
	6 Preliminary definitions and concepts in point pattern analysis
		6.1 Spatial point patterns of economic agents
		6.2 The hypothesis of complete spatial randomness
		6.3 Spatial point processes
			6.3.1 Homogeneous Poisson point process
			6.3.2 Aggregated point processes
				6.3.2.1 Inhomogeneous Poisson point processes
				6.3.2.2 Cox processes
				6.3.2.3 Poisson cluster point processes
			6.3.3 Regular point processes
		6.4 Classic exploratory tools and summary statistics for spatial point patterns
			6.4.1 Quadrat-based methods
			6.4.2 Distance-based methods
	7 Models of the spatial location of individuals
		7.1 Ripley’s K-function
		7.2 Estimation of Ripley’s K-function
		7.3 Identification of spatial location patterns
			7.3.1 The CSR test
			7.3.2 Parameter estimation of the Thomas cluster process
			7.3.3 Parameter estimation of the Matérn cluster process
			7.3.4 Parameter estimation of the log-Gaussian Cox process
	8 Points in a heterogeneous space
		8.1 Diggle and Chetwynd’s D-function
		8.2 Baddeley, Møller and Waagepetersen’s K[sub(inhom)] -function
			8.2.1 Estimation of K[sub(inhom)]-function
			8.2.2 Inference for K[sub(inhom)] -function
		8.3 Measuring spatial concentration of industries: Duranton–Overman K-density and Marcon–Puech M-function
			8.3.1 Duranton and Overman’s K-density
			8.3.2 Marcon and Puech’s M-function
	9 Space–time models
		9.1 Diggle, Chetwynd, Häggkvist and Morris’ space–time K-function
			9.1.1 Estimation of space–time K-function
			9.1.2 Detecting space–time clustering of economic events
		9.2 Gabriel and Diggle’s STIK-function
			9.2.1 Estimation of STIK-function and inference
PART IV: Looking ahead: modeling both the spatial location choices and the spatial behavior of economic agents
	10 Firm demography and survival analysis
		10.1 Introduction
		10.2 A spatial microeconometric model for firm demography
			10.2.1 A spatial model for firm demography
				10.2.1.1 Introduction
				10.2.1.2 The birth model
				10.2.1.3 The growth model
				10.2.1.4 The survival model
			10.2.2 A case study
				10.2.2.1 Data description
				10.2.2.2 The birth model
				10.2.2.3 The growth model
				10.2.2.4 The survival model
			10.2.3 Conclusions
		10.3 A spatial microeconometric model for firm survival
			10.3.1 Introduction
			10.3.2 Basic survival analysis techniques
			10.3.3 Case study: The survival of pharmaceutical and medical device manufacturing start-up firms in Italy
				10.3.3.1 Data description
				10.3.3.2 Definition of the spatial microeconometric covariates
				10.3.3.3 Definition of the control variables
				10.3.3.4 Empirical results
		10.4 Conclusion
Appendices
	Appendix 1: Some publicly available spatial datasets
	Appendix 2: Creation of a W matrix and preliminary computations
	Appendix 3: Spatial linear models
	Appendix 4: Non-linear spatial models
	Appendix 5: Space–time models
	Appendix 6: Preliminary definitions and concepts in point pattern analysis
		Appendix 6.1: Point pattern datasets
		Appendix 6.2: Simulating point patterns
			Appendix 6.2.1: Homogeneous Poisson processes
			Appendix 6.2.2: Inhomogeneous Poisson processes
			Appendix 6.2.3: Cox processes
			Appendix 6.2.4: Poisson cluster processes
			Appendix 6.2.5: Regular processes
		Appendix 6.3: Quadrat-based analysis
		Appendix 6.4: Clark–Evans test
	Appendix 7: Models of the spatial location of individuals
		Appendix 7.1: K-function-based CSR test
		Appendix 7.2: Point process parameters estimation by the method of minimum contrast
	Appendix 8: Points in a heterogeneous space
		Appendix 8.1: D-function-based test of spatial interactions
		Appendix 8.2: K -function-based test of spatial inhom interactions
		Appendix 8.3: Duranton–Overman K-density and Marcon–Puech M-function
	Appendix 9: Space–time models
		Appendix 9.1: Space–time K-function
		Appendix 9.2: Gabriel and Diggle’s STIK-function
Bibliography
Index