Departamento de Economía Universidad de Chile
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1 Departamento de Economía Universidad de Chile GRADUATE COURSE SPATIAL ECONOMETRICS November 14, 16, 17, 20 and 21, 2017 Prof. Henk Folmer University of Groningen Objectives The main objective of the course is to introduce the students to spatial econometrics and its applications to economics and social problems. Spatial Econometrics Generally speaking, spatial econometrics is the application of econometric techniques to data associated with spatial units. Typical for spatial units is that they interact. That is, i.e. an observation associated with a given location, say i, depends on observations at locations other than i. For instance, employment growth in one region influences, and is influenced by, employment growth in other regions. Whereas in time series analysis the influence is one directional, i.e. from the past to the presence, in a spatial setting it is multidirectional. This feature usually renders straightforward application of standard econometric techniques to spatial data invalid. The typical feature of spatial data has been a central theme in quantitative geography, regional science and regional economics. However, spatial data is rapidly becoming available in many subfields of economics including international economics, labor market economics, public economics and environmental economics. Moreover, spatial econometric techniques are being applied in other fields where the units of observation interact, e.g. network analysis with applications in general economics, business economics but also in other social sciences including sociology and psychology. Furthermore, a growing number of social scientists has taken up the use of new methodologies and technologies (such as geographic information systems, global positioning systems and remote sensing) in empirical work which has led to an increase of data sets characterized by spatial dependence. Also in the natural sciences like biology, ecology and epidemiology spatial econometric techniques are routinely applied. The purpose of the course is to present an introduction to the basic features of spatial data, an outline of the pitfalls of straightforwardly applying standard econometric techniques to spatial data, and an overview of techniques to account for spatial data characteristics in regression analysis. Given the time constraint the introduction will be restricted to cross section data. However, the cross section techniques form the base of panel data analysis and limited dependent variables. Insight into the basics of cross section techniques is required for understanding the basics of more advanced techniques The (tentative) class time table and content of the lectures is as follows. 1
2 CLASS TIME TABLE Day 1); November Tuesday 14: Chapters 1, 2, 3: 3.1 Morning: + Class 1: 09:00-10:10 + Class 2: 10:30:-11:40 + Discussion Session 1: 11:40-12:10 Afternoon: + Class 3: 14:30-15:40 + Class 4 16:00:-17:10 + Discussion Session 2: 17:10-17:40 (Day 2); November Thursday 16: Chapters 3:3.2, 3.3; 4: 4.1, 4.2. Morning: + Class 5: 09:00-10:10 + Class 6: 10:30:-11:40 + Discussion Session 3: 11:40-12:10 Afternoon: + Class 7: 14:30-15:40 + Class 8 16:00:-17:10 + Discussion Session 4: 17:10-17:40 (Day 3); November Friday 17: Chapters 4: 4.3, 4.4; 5: 5.1, Morning: + Class 5: 09:00-10:10 + Class 6: 10:30:-11:40 + Discussion Session 3: 11:40-12:10 Afternoon: + Class 7: 14:30-15:40 + Class 8 16:00:-17:10 + Discussion Session 4: 17:10-17:40 Day 4; (Monday) November 20: Chapter 5: Morning: + Class 13: 09:00-10:10 + Class 14: 10:30:-11:40 + Discussion Session 7: 11:40-12:10 Afternoon: + Class 15: 14:30-15:40 + Class 16 16:00:-17:10 + Discussion Session 8: 17:10-17:40 Day 5; (Tuesday) November 21: Chapter 5: 5.4 Morning: + Class 17: 09:00-10:10 + Class 18: 10:30:-11:40 + Discussion Session 9: 11:40-12:10 CLASS CONTENT Day 1); November Tuesday 14: Chapters 1,2, 3: Introduction 1.1. Why Spatial Econometrics 1.2. Spatial Dependence 1.3. Spatial Heterogeneity 2. Modeling connectivity in Space 2.1. Neighbor in Space 2.2. Spatial Weights Matrix 2.3. Spatial Lag 2.4. Spatial Autocorrelation Global Autocorrelation Local Autocorrelation 2.5. Spatial Cross Correlation 2
3 Global Cross Correlation Local Cross Correlation 3. Spatial Econometric Models 3.1. A Taxonomy of Linear Spatial Dependence Models Standard linear regression model (LM) Spatial dependence of exogenous variables (SLX) Spatial Lag Dependence (SpLag) Spatial Error Dependence (SEM) (Day 2); November Thursday 16: Chapters 3:3.2, 3.3; 4: 4.1, The consequences of ignoring spatial dependence Biasedness Inconsistency Inefficiency 3.3. Testing for Spatial Dependence The Moran s I test Lagrange Multiplier (LM) tests Robust Tests for Spatial Dependence F test for omitted spatially lagged exogenous variables 4. Frequentist Estimation, Testing and Interpretation of Cross-Sectional Spatial Econometric Models 4.1. SLX Ordinary Least Square (OLS) 4.2. SpLag Instrumental Variables Maximum Likelihood (ML) Quasi Maximum Likelihood (QML) Generalized Method of Moments (GMM) (Day 3); November Friday 17: Chapters 4: 4.3, 4.4; 5: 5.1, SEM ML GMM 4.4. Interpretation and Testing of Spatial Spillover Effects 5. Bayesian Estimation and Testing of Cross-Sectional Non-Spatial and Spatial Econometric Models 5.1. Introduction to the Bayesian Methodology Problems with ML Advantages of the Bayesian Approach Basic Axioms of Probability Theory Prior Distribution Areas of Application of Bayesian Methods Inference Model Comparison 3
4 Prediction 5.2. Bayesian Estimation Methods Analytical Approach Simulation Approach Monte Carlo (MC) Markov Chain (MC) MCMC Metropolis Algorithm Metropolis Hasting Algorithm Gibbs Sampler Day 4; (Monday) November 20: Chapters 5: Numerical Approach: Integrated Nested Laplace Approximation (INLA) Laplace Transformation INLA setting: The Class of latent Gaussian models Bayesian Inference with INLA INLA Step-by-step 5.3. Bayesian Non-Spatial Regression Bayesian Specification of the Linear Regression Model Inference of the Bayesian Linear Model Analytical Approach Simulation Approach (MCMC) Numerical Approach (INLA) Day 5; (Tuesday) November 21: Chapter 5: Bayesian Spatial Regression SpLag SEM END OF COURSE CLASSES OTHER TOPICS OF THE COURSE (MATERIAL PROVIDED) Chapter 6 6. Panel and Spatial Panel Models 6.1. Panel Models Advantages of Panel Models Estimation and Testing of Non-Dynamic Panel Models The Fixed Effects Model (FE) The Random Effects Model (RE) FE Versus RE 6.2. Dynamic Panel Models Arellano and Bond Estimator 6.3. Spatial Panel Models Non-Dynamic Spatial Panel Models Fixed Effects Spatial Lag Model (FESpLag) Fixed Effects Spatial Error Model (FESEM) Random Effects Spatial Lag Model (RESpLag) Random Effects Spatial Error Model (RESEM) Model Comparison and Prediction 4
5 Model Interpretation Dynamic Spatial Panel Models Taxonomy of Dynamic Models in Space and Time Estimation of Dynamic Models ML GMM Chapter 7 7. Bayesian Panel and Spatial Panel Models 7.1. Bayesian Hierarchical Models Pooled Model Non-Hierarchical Model Hierarchical Model 7.2. Prior Structure Panel Models Pooled Model Fixed Effect Model Random Effect Model 7.3. Bayesian Non-Dynamic Panel Models Pooled Model Individual Effects Model FE RE Heterogeneous coefficients 7.4. Bayesian Dynamic Panel Model Dynamic FE Dynamic RE 7.5 Bayesian Non-Dynamic Spatial Panel Models FESpLag RESpLag FESEM RESEM 7.6. Bayesian Non-Dynamic Spatial Panel Model with Heterogeneous Coefficients Heterogeneous coefficients FESpLag Heterogeneous coefficients RESpLag Heterogeneous coefficients FESEM Heterogeneous coefficients RESEM 7.7. Bayesian Dynamic Spatial Panel Models Dynamic FESpLag Dynamic RESpLag Dynamic FESEM Dynamic RESEM Chapter 8 8. Spatial Limited Dependent Variable Models 8.1. The a-spatial Logit and Probit models Estimation and Inference of the a-spatial Logit and Probit Model 8.2. The Spatial Probit and Logit Models Estimation and Inference of the Spatial Probit and Logit Models ML GMM Bayesian estimation 5
6 Linearized GMM 8.3. The Spatial Count Data Model 8.4. The Spatial Tobit Models Chapter 9 9. Model Selection in (Spatial) Econometrics 9.1. Model Evaluation Criteria 9.2. Methods of Model Selection Classical hypothesis testing based procedures Information based criteria Penalized-likelihood Methods The Akaike Information Criterion (AIC) The AIC corrected criterion (AICc) The Bayesian Information Criterion (BIC) Bayesian Model Selection Bayes Factor (BF) Deviance Information Criterion (DIC) 6
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