Logotipo librería Marcial Pons
Structural econometric modeling in industrial organization and quantitative marketing

Structural econometric modeling in industrial organization and quantitative marketing
theory and applications

  • ISBN: 9780691243467
  • Editorial: Princeton University Press
  • Lugar de la edición: Princeton (NJ). Estados Unidos de Norteamérica
  • Encuadernación: Cartoné
  • Medidas: 27 cm
  • Nº Pág.: 280
  • Idiomas: Inglés

Papel: Cartoné
67,87 € 53,95 €
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Resumen

A concise and rigorous introduction to widely used approaches in structural econometric modeling

Structural econometric modeling specifies the structure of an economic model and estimates the model's parameters from real-world data. Structural econometric modeling enables better economic theory-based predictions and policy counterfactuals. This book offers a primer on recent developments in these modeling techniques, which are used widely in empirical industrial organization, quantitative marketing, and related fields. It covers such topics as discrete choice modeling, demand modes, estimation of the firm entry models with strategic interactions, consumer search, and theory/empirics of auctions. The book makes highly technical material accessible to graduate students, describing key insights succinctly but without sacrificing rigor.

1. Introduction: Structural Econometric Modeling
1.1 Model
1.1.1 Scientific Model and Economic Model
1.1.2 Predictive Model and Causal Model
1.2 Econometrics
1.3 Structure
1.4 Debate around the Structural Econometric Modeling Approach
1.5 Outline of This Book
2. Static and Dynamic Discrete Choice
2.1 Binary Choice
2.1.1 Motivation: Linear Probability Model
2.1.2 Binary Logit and Binary Probit Model
2.1.3 Marginal Effects
2.2 Multiple Choice: Random Utility Maximization Framework 2.2.1 Preliminary Results: Type I Extreme Value Distribution and Its Properties
2.2.2 The Simple Logit Model
2.2.3 Independence of Irrelevant Alternatives and the Nested Logit Model
2.2.4 Discussion
2.3 Single-Agent Dynamic Discrete Choice
2.3.1 Full-Solution Method with Fixed-Point Iteration
2.3.2 Estimation with Conditional Choice Probability Inversion
2.3.3 Nested Pseudo-Likelihood Estimation
2.3.4 Extension to Incorporate Unobserved State Variables
2.3.5 (Non)-identification of the Discount Factor
3. Demand Estimation Using Market-Level Data 3.1 Product-Space Approach
3.1.1 Linear and Log-Linear Demand Model
3.1.2 The Almost Ideal Demand System
3.1.3 Further Discussion of the Product-Space Approach
3.2 Characteristics-Space Approach I: Static Logit Demand Models
3.2.1 Microfoundation: Discrete-Choice Random Utility Maximization
3.2.2 Logit Demand Models with Aggregate Market Data
3.2.3 Further Discussion of the Static Logit Demand Models
3.3 Characteristics-Space Approach II: Extensions of the Static Logit Demand Models
3.3.1 Accommodating Zero Market Shares 3.3.2 Characteristics-Space Approach without Random Utility Shocks
4. Estimation of Discrete-Game Models
4.1 Estimation of Discrete-Game Models with Cross-Sectional Data
4.1.1 Static Discrete Games with Complete Information
4.1.2 Static Discrete Games with Incomplete Information
4.1.3 Further Discussion of the Estimation of Game Models with Cross-Sectional Data
4.2 Estimation of Dynamic Discrete-Game Models
4.2.1 Industry Dynamics in an Oligopolistic Market and the Markov Perfect Equilibrium
4.2.2 Estimation Frameworks of Dynamic Discrete Games 4.2.3 Further Issues and Discussion of Dynamic Game Models Estimation
5. Empirical Frameworks of Consumer Search
5.1 Utility Specification and Some Preliminary Results
5.1.1 Utility Specification in Consumer Search Models
5.1.2 Some Preliminary Results on Stochastic Dominance
5.2 Classical Search-Theoretic Models: Sequential Search and Simultaneous Search
5.2.1 Sequential Search
5.2.2 Simultaneous Search
5.3 Price Dispersion in the Market Equilibrium and Search Cost Identification with Price Data

Resumen

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