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Spatial Microeconometrics

Foundations of spatial microeconometrics modeling A micro-level approach to spatial econometrics Advantages of spatial microeconometric analysis Sources of spatial micro-data Sources of uncertainty in spatial micro-data Conclusions and plan of the book II: Modeling the spatial behavior of economic agents in a given set of locations Preliminary definitions and concepts Neighborhood and the W matrix Moran’s I and other spatial correlation measures The Moran scatterplot and local indicators of spatial correlation Conclusions Basic cross-sectional spatial linear models Introduction Regression models with spatial autoregressive components Pure spatial autoregression The spatial error model The spatial lag model The spatial Durbin model The general spatial autoregressive model with spatial autoregressive error structure Test of residual spatial autocorrelation with explicit alternative hypotheses Marginal impacts Effects of spatial imperfections of micro-data Introduction Measurement error in spatial error models Measurement error in spatial lag models Problems in regressions on a spatial distance Non-linear spatial models Non-linear spatial regressions Standard non-linear models Logit and probit models The tobit model Spatial probit and logit models Model specification Estimation The spatial tobit model Estimation Further non-linear spatial models Marginal impacts in spatial non-linear models Space–time models Generalities Fixed and random effects models Random effects spatial models Fixed effect spatial models Estimation Introduction Maximum likelihood Likelihood procedures for random effect models Likelihood procedures for fixed effect models The generalized method of moments approach Generalized method of moments procedures for random effects models Generalized method of moments procedures for fixed effects models A glance at further approaches in spatial panel data modeling III: Modeling the spatial locational choices of economic agents Preliminary definitions and concepts in point pattern analysis Spatial point patterns of economic agents The hypothesis of complete spatial randomness Spatial point processes Aggregated point processes Inhomogeneous Poisson point processes Poisson cluster point processes Regular point processes Classic exploratory tools and summary statistics for spatial point patterns Quadrat-based methods Distance-based methods Models of the spatial location of individuals Ripley’s K-function Estimation of Ripley’s K-function Identification of spatial location patterns The CSR test Parameter estimation of the Thomas cluster process Parameter estimation of the Matérn cluster process Parameter estimation of the log-Gaussian Cox process Points in a heterogeneous space Diggle and Chetwynd’s D-function Baddeley, Møller and Waagepetersen’s K[sub(inhom)] -function Estimation of K[sub(inhom)]-function Inference for K[sub(inhom)] -function Measuring spatial concentration of industries: Duranton–Overman K-density and Marcon–Puech M-function Duranton and Overman’s K-density Marcon and Puech’s M-function Space–time models Diggle, Chetwynd, Häggkvist and Morris’ space–time K-function Estimation of space–time K-function Detecting space–time clustering of economic events Gabriel and Diggle’s STIK-function Estimation of STIK-function and inference IV: Looking ahead: modeling both the spatial location choices and the spatial behavior of economic agents Firm demography and survival analysis Introduction A spatial microeconometric model for firm demography A spatial model for firm demography Introduction The birth model The growth model The survival model A case study Data description The birth model The growth model The survival model Conclusions A spatial microeconometric model for firm survival Introduction Basic survival analysis techniques Case study: The survival of pharmaceutical and medical device manufacturing start-up firms in Italy Data description Definition of the spatial microeconometric covariates Definition of the control variables Empirical results Conclusion Appendices Appendix 1: Some publicly available spatial datasets Appendix 2: Creation of a W matrix and preliminary computations 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 8.3: 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

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