Spatial microeconometrics / Giuseppe Arbia, Giuseppe Espa and Diego Giuliani.

Format:

Book

Author/Creator:

Arbia, Giuseppe, author.

Espa, Giuseppe, author.

Giuliani, Diego, author.

Series:

Routledge advanced texts in economics and finance ; 34.

Routledge Advanced Texts in Economics and Finance ; 34

Language:

English

Subjects (All):

Microeconomics--Econometric models.

Microeconomics.

Physical Description:

1 online resource (251 pages).

Edition:

1st ed.

Place of Publication:

London ; New York, New York : Routledge, [2021]

Summary:

Spatial Microeconometricsintroduces the reader to the basic concepts of spatial statistics, spatial econometrics and thespatial 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 outlinesthe specificities of data that represent a set of interacting individuals with respect to traditional econometricsthat 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 howto 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.

Contents:

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.

Notes:

Description based on print version record.

ISBN:

1-317-56347-6

1-315-73527-X

1-317-56348-4

9781315735276

OCLC:

1246250828