The Econometrics of Networks.

Intro -- Half Title Page -- Series editors Page -- Title Page -- Copyright Page -- Contents -- Introduction: Econometrics of Networks -- Section 1: Identification of Network Models -- Section 2: Network Formation -- Section 3: Networks and Spatial Econometrics -- Section 4: Applications of Financial Networks -- References -- SECTION 1: IDENTIFICATION OF NETWORK MODELS -- Chapter 1: IDENTIFICATION AND ESTIMATION OF NETWORK MODELS WITH HETEROGENEOUS INTERACTIONS -- 1. Introduction -- 2. The Network Model with Heterogeneous Peer Effects -- 3. Identification -- 4. The 2SLS Estimator -- 4.1. Asymptotic Properties -- 4.2. Finite Sample Performance -- 5. Model Misspecification Bias -- 6. Conclusion -- References -- Chapter 2: IDENTIFICATION METHODS FOR SOCIAL INTERACTIONS MODELS WITH UNKNOWN NETWORKS -- 1. Introduction -- 1.1. Review on the Identification Literature and Its Connection to the Proposed Methods -- 1.2. Review on the Estimation Literature -- 1.3. Basic Assumptions -- 1.4. Notation -- 2. Long Panel Identification -- 2.1. Fixed Effect Elimination -- 2.2. Models with Time-Specific Fixed Effects -- 3. Short Panel Identification -- 3.1. Models without Time-Specific Fixed Effects -- 3.1.1. An Example -- 3.2. Models with Time-Specific Fixed Effects -- 4. Concluding Remarks -- References -- Chapter 3: SNOWBALL SAMPLING AND SAMPLE SELECTION IN A SOCIAL NETWORK -- 1 Introduction -- 2 Setup -- 3 Estimation -- 3.1 Moment Equation -- 3.2 Sample Moment Equation -- 3.3 Asymptotic Distribution -- 3.4 More Than Two Types -- 3.5 Optimal Weighting -- 4 Simulation -- 4.1 Data Generating Process: Stochastic Block Model -- 4.2 Data Generating Process: Subsample of Social Network Data -- 5 Empirical Application -- 6 Conclusion -- 7 Proof -- References -- SECTION 2: NETWORK FORMATION -- Chapter 4: TRADE NETWORKS AND THE STRENGTH OF STRONG TIES -- 1. Introduction.

2. Review of the Literature -- 2.1. A Network Formation Game -- 2.2. Examples -- 2.3. The Literature -- 3. A Trade Network -- 3.1. The Basic Setup -- 3.2. Imperfect Reliability of Links -- 3.2. Strategic Establishment of Ties -- 3.4. The Strength of Strong Ties -- 4. Conclusion -- References -- Appendix -- Expected Utility for an n-member Market -- Expected utility for an unreliable complete n-network -- Chapter 5: APPLICATION AND COMPUTATION OF A FLEXIBLE CLASS OF NETWORK FORMATION MODELS -- 1. Introduction -- 2. Utility and Network Positions -- 2.1. Friendship Model -- 2.2. Coauthorship Model -- 3. Aggregate Equilibrium Conditions -- 3.1. Aggregation Approach -- 3.2. Adding Local Links -- 3.3. Adding Self-links -- 4. Statistical Inference -- 5. Computational Simplifications -- 5.1. Restriction to Observed Network Types -- 5.2. Residual Categories of Types -- 6. Recovered Sets -- 6.1. Simulation of the Friendship Model -- 6.2. Coauthorship Model -- 7. Conclusion -- References -- SECTION 3: NETWORKS AND SPATIAL ECONOMETRICS -- Chapter 6: IMPLEMENTING FAUSTMANN-MARSHALL-PRESSLER AT SCALE: STOCHASTIC DYNAMIC PROGRAMING IN SPACE -- 1 Motivation and Introduction -- 2 Previous Theoretical Structure -- 2.1 Biological Environment -- 2.2 Maximum Sustainable Yield -- 2.3 Lumber Production -- 2.4 Rent Extraction -- 2.5 Site Heterogeneity -- 2.6 Faustmann-Marshall-Pressler Solution -- 2.7 Common Criticisms of the FMP Framework -- 3 Geographical, Intertemporal, and Stochastic Model -- 3.1 Recursive Solution Via the Method of Dynamic Programing -- 3.1.1 Assumptions concerning the Economic and Physical Environment -- 3.1.2 Solution Strategy -- 3.1.3 Bellman's Equation -- 4 Geographic Information System -- 5 Modeling Growth and Yield in Stands of Timber -- 5.1 Predicting Growth and Yield in Planted Forests: TASS.

5.2 Predicting Growth and Yield in Old-Growth Forests: VDYP -- 6 Implementation -- 6.1 Some Relevant Features of the Data Set -- 6.2 Computational Issues -- 6.3 Some Illustrative Results -- 7 Summary and Conclusions -- References -- Chapter 7: A SPATIAL PANEL MODEL OF BANK BRANCHES IN CANADA -- 1. Introduction -- 1.1. Disaggregated Area Analysis -- 1.2. Spatial Dependence -- 1.3. Geographical Concentration -- 2. Data -- 2.1. Payments Canada Financial Institution File -- 2.1.1. Geographic Concentration -- 2.1.2. Industrial Concentration -- 2.2. Canadian Census -- 2.3. Statistics Canada Catographic Boundary File -- 3. Spatial Panel Model -- 3.1. Empirical Results - Baseline Models -- 3.1.1. Results - All Banks -- 3.1.2. Results - Small Banks -- 3.1.3. Results - Big Banks -- 3.2. Main Results - All Banks -- 3.2.1. Results -- 3.3. Empirical Results - Big Five and Small Banks -- 3.3.1. Results -- 4. Robustness -- 4.1. Longer 10 Years Panel Without Socioeconomic Variables -- 4.2. Census Metropolitan Area Results -- 5. Conclusion -- References -- APPENDIX -- 1. Regression Results (Baseline Models) -- 2. Regression Results (3 Years) -- 4. Regression Results (CMA Level) -- Chapter 8: FULL-INFORMATION BAYESIAN ESTIMATION OF CROSS-SECTIONAL SAMPLE SELECTION MODELS -- 1. Introduction -- 2. Econometric Model -- 3. Posterior Inference -- 3.1 The Priors -- 3.2 Likelihood and Conditional Posterior Distributions -- 3.3 Gibbs Sampling of the Parameters of Interest -- 3.3.1 Sampling the Vector of Latent Variables Y* -- 3.3.2 Conditional Posterior Distribution of β -- 3.3.3 Conditional Posterior Distribution of ∑ -- 3.3.4 Conditional Posterior Distribution of ρA and ρB -- 4. Markov-chain Monte Carlo Estimation -- 5. Monte Carlo Evidence -- 5.1 Design -- 5.2 Results -- 6. Conclusion -- References -- 7. Appendix -- 7.1 The Prior -- 7. 2 Deriving p(Y* |θ).

7. 3 Deriving the Conditional Distributions of Blocks of theLatent Variable: yA*| yB*, θ, yB*| yA*, θ -- 7. 4 Conditional Posterior Distribution of Σ -- 7. 5 Conditional Posterior Distribution of ρA and ρB -- 7. 6 Augmenting Data by Latent Variable Y* -- 7. 7 Drawing the Variance-Covariance Matrix of the Idiosyncratic Error Σ -- 7. 8 Drawing the Spatial Autocorrelation Parameter ρ -- 7. 9 Computation of Determinants |RA|, |RB| -- 7. 10 Results of Full-information Standard Heckman Models Assuming ρℓ = 0 -- Chapter 9: SURVIVAL ANALYSIS OF BANK NOTE CIRCULATION: FITNESS, NETWORK STRUCTURE, AND MACHINE LEARNING -- 1 Introduction -- 2 Single Note Inspection Data -- 2.1 Institutional Background -- 2.2 Structure of the IMS Dataset -- 2.3 Sample Description -- 3 The Network and Spatial Patterns of Bank Notes -- 3.1 The Cycle Duration and Bank Note Fitness -- 3.2 Money Circulation Network -- 4 Banknote Clusters -- 5 Hazard Model for Bank Notes -- 6 Conclusion -- References -- SECTION 4: APPLICATIONS OF FINANCIALNETWORKS -- Chapter 10: FINANCIAL CONTAGION IN CROSS-HOLDINGS NETWORKS: THE CASE OF ECUADOR -- 1. Introduction -- 2. Literature review -- 3. The financial contagion model -- 3.1. The Book and Market Values of a Firm -- 3.2. The Effect of Cascades of Failures -- 3.3. Diversification, Integration, and Homophily -- 4. Data description -- 4.1. Cross-holding Matrix and Values of Organizations3 -- 4.2. Dependency Matrix -- 4.3. Simulation Parameters -- 5. Results -- 5.1. Network Structure -- 5.2. Cascades Effects: One Firm at a Time -- 5.3. What if an Entire Industry Collapses? -- 6. Conclusions -- References -- Chapter 11: ESTIMATING SPILLOVER EFFECTS WITH BILATERAL OUTCOMES -- 1. Introduction -- 2. A Network of Decentralized Exchanges -- 3. Node-based SAR -- 4. Link-based SAR -- 4.1. Identification -- 4.2. 2SLS Estimation -- 5. Monte Carlo Study.

5.1. Setting -- 5.2. Results -- 6. Empirical Example -- 6.1. Data -- 6.2. Estimates -- 7. Conclusion -- References -- Chapter 12: INTERCONNECTEDNESS THROUGH THE LENS OF CONSUMER CREDIT MARKETS -- 1. Introduction -- 2. Stylized Facts -- 2.1. Data -- 2.2. Distributions of Consumer Loans -- 3. Overlapping Portfolio of Consumer Loans -- 3.1. Network Construction -- 3.2. Overall Connectedness -- 3.3. Connectedness by FI Type -- 3.4. Connectedness by Credit Market -- 4. Factors Influencing FI-level Interconnectedness -- 5. Conclusion -- References -- Chapter 13: FRM FINANCIAL RISK METER -- 1. Introduction -- 2. FRM Systemic Risk Measure Framework -- 2.1. Modeling Framework -- 2.2. Financial Risk Meter -- 3. FRM Data and Computational Characteristics -- 3.1. FRM Data Compilation -- 3.2. FRM Computational Characteristics -- 4. Economic Applications -- 4.1. Co-stress, Activators and Network Dynamics -- 4.2. The SRM -- 4.3. FRM and Credit Default Swaps -- 4.4. FRM as the Predictor of Recessions -- 4.5. Further implications and extensions -- 5. Conclusions -- References -- Index.

Showcasing fresh methodological and empirical research on the econometrics of networks, and comprising both theoretical, empirical and policy papers, the authors in this volume bring together a wide range of perspectives to facilitate a dialogue between academics and practitioners for better understanding this groundbreaking field.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.