Introduction to the Structural Econometrics of Auction Data

ISBN-10: 0262162350
ISBN-13: 9780262162357
Edition: 2006
List price: $50.00
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Description: This text, intended for both graduate students and professional researchers, is an effective, concise introduction to the structural econometrics of auctions. Tools from recent developments in theoretical econometrics are combined with established  More...

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Book details

List price: $50.00
Copyright year: 2006
Publisher: MIT Press
Publication date: 1/6/2006
Binding: Hardcover
Pages: 694
Size: 7.00" wide x 9.00" long x 1.00" tall
Weight: 1.936
Language: English

This text, intended for both graduate students and professional researchers, is an effective, concise introduction to the structural econometrics of auctions. Tools from recent developments in theoretical econometrics are combined with established numerical methods to provide a practical guide to most of the main concepts in the empirical analysis of field data from auctions. Among other things, the text is remarkable for a large number of mathematical problems and computer exercises for which sample solutions are provided at the end of the book. In the case of the computer exercises, sample code written in Matlab provides a ready-made toolbox that allows readers to implement many existing empirical specifications efficiently. In the first two chapters, the authors introduce several important issues in the analysis of field data from auctions and then go on to develop a simple theoretical model within the independent, private-values paradigm. In the third chapter, under several data-generating schemes, the authors outline empirical methods for analyzing data from single-unit Vickrey and English auctions, while in the fourth chapter, they outline methods for analyzing data from single-unit, Dutch, and first-price sealed-bid auctions. In the fifth chapter, the authors discuss theoretical issues important in the analysis of multi-good auctions, focusing on the analysis of multi-unit auctions, and then provide examples of some recent strategies designed to analyze data from these auctions. Included at the end are a number of appendixes that review the technical tools required in developing the topics treated in the text. A CD-ROM containing sample computer code and data sets accompanies the text.

Harry J. Paarsch is Professor of Economics and Robert Jensen Research Fellow at the Henry B. Tippie College of Business, University of Iowa.

Han Hong is Associate Professor of Economics at Duke University.

Focus of the Book
Who Should Read This Book?
Material We Intend to Presume
What a Learner Will Know
Comments and Suggestions
An Example
Some Intriguing Problems
Plan of Book
Practice Problems
Overview of Auction Theory
Auction Formats and Rules
Oral, Ascending-Price (English) Format
First-Price, Sealed-Bid Format
Oral, Descending-Price (Dutch) Format
Second-Price, Sealed-Bid (Vickrey) Format
Additional Rules
Information Structure
Independent Private-Values Paradigm
Common-Value Paradigm
Affiliated-Values Paradigm
Bidder Preferences
Purposeful Bidding and Game Equilibrium
Vickrey Auctions
English Auctions
Dutch and First-Price, Sealed-Bid Auctions
A Binding Reserve Price
Expected Revenues and Optimal Auctions
Revenue Equivalence Proposition
Optimal Reserve Price
Expected Revenues under Risk Aversion
Optimal Auctions
Winner's Curse
Practice Problems
Vickrey and English Auctions
Nonparametric Identification and Estimation
Single-Index Models: Semiparametric Estimation
Density-Weighted Derivative Estimator
Maximum Rank-Correlation Estimator
Reserve Prices and Parametric Estimation
Policy Application
Some Institutional Background
Bidding of Timber in British Columbia
Defense of IPVP Assumption
Mapping Observables into Bids
Deriving the Empirical Specification
Implementing the Empirical Specification
Estimates of the Optimal Reserve Price
Incomplete Data
Incomplete Inference
Asymmetric Bidders
Nonparametric Identification
Semi-Nonparametric Estimation
Practice Problems
First-Price, Sealed-Bid and Dutch Auctions
Deriving the Data-Generating Process
Identification and Estimation: Simplest Case
Nonparametric Identification
Nonparametric Estimation
Maximum Likelihood Estimation
Simulated Nonlinear Least-Squares
Extreme-Order, Generalized Method-of-Moments
Binding Reserve Price
Risk-Averse Bidders
Parametric Identification and Estimation
Semiparametric Identification and Estimation
Stochastic Private Values
Asymmetric Bidders
First-Price, Sealed-Bid Auctions
Dutch Auctions
Unobserved Heterogeneity
Policy Experiment
Some Institutional Background
Empirical Results
Comparing Institutions
Another Way to Solve Asymmetric Auctions
Endogenous Bidding Participation
Practice Problems
Multi-Unit Auctions
Weber's Classification System
Simultaneous-Dependent Auctions
Simultaneous-Independent Auctions
Sequential Auctions
Pricing Rules
Shading and Demand Reduction
Generalized Vickrey Auction
Ausubel Auction
Singleton Demand
Multi-Unit Demand
Nonrandom Demand
Random Demand
Sequential, Clock Auctions: Random Demand
Two-Bidder, Two-Unit Example
Two-Bidder, Three-Unit Example
Demand-Generation Scheme
Solving the Auction Game
Properties of the Equilibrium
Estimating the Model
Sequential, Clock Auctions: Nonrandom Demand
Nonparametric Identification
Semi-Nonparametric Estimator
Practice Problems
Directions for Further Research
Simultaneous, Dependent, Sealed-Bid Auctions
Infinitely Lived Auction Games
Multi-Object Auctions
Transformations of Random Variables
Linear Transformations
Monotonic Nonlinear Transformations
Order Statistics
Independent and Identically Distributed Case
General Independent Case
Distribution of Cumulative Distribution Functions
Generating Random Numbers
Pseudo-Random Numbers
Asymptotic Methods: Some Elementary Tools
Parameter Consistency: Laws of Large Numbers
Asymptotic Normality: Central Limit Theorems
Continuous Mapping Theorem
Delta Method
Extreme Value Theorems
Evaluating Estimators
Finite-Sample Properties
Mean Squared-Error
Large-Sample Properties
Parameter Consistency
Asymptotic Distribution
The Bootstrap
Estimation Strategies
Generalized Method of Moments
Parameter Consistency/Asymptotic Normality
Testing Over-Identifying Restrictions
Method of Maximum Likelihood
Parameter Consistency/Asymptotic Normality
Nonparametric Estimation
Kernel-Smoothed Density Functions
Empirical Distribution Function
Bandwidth Choice
Asymptotic Distribution of Order Statistics
Characteristic Functions: Deconvolution Methods
Numerical Methods
Finding the Zero of a Function
Bisection Method
Newton's Method
Unconstrained Optimization
Constrained Optimization
Numerical Integration
Newton-Cotes Formulae
Approximation Methods
A High-Level Programming Environment
Getting Started
The Interface
Loading and Cataloging Data
Manipulating Data
Basic Matrix Functions
Linear Systems
Two-Dimensional Plotting
Three-Dimensional Plotting
Statistics Toolbox
Descriptive Statistics
Pseudo-Random Number Generators
Probability Density Functions
Cumulative Distribution Functions
User-Defined Functions
A Generic Function
Writing a User-Defined Function
Another User-Defined Function
Optimization Toolbox
Driver Files and Objective Functions
Gradient Vectors and Hessian Matrices
Unconstrained Optimization
Constrained Optimization
Algorithm Options: optimset
Logarithmic Series
Log-Normal Estimation
Logarithmic Series with Covariates
Practice with the Bootstrap
Approximating Functions
Approximating Integrals
Uniform Case: Risk Neutrality, No Reserve
Uniform Case: Risk Neutrality, Positive Reserve
Uniform Case: Risk Aversion, No Reserve
Procurement with Independent, Private Costs
All-Pay Auction
Test Scores within the Location-Scale Family
Vickrey Auctions and the Location-Scale Family
ML Estimation of Vickrey Auctions
Nonparametric Estimation of English Auctions
English Auctions: Incomplete Data and Inference
Uniform Case: First-Price, Sealed-Bid Auctions
Pareto Case: Low-Price, Sealed-Bid Auctions
Government Procurement
Solving Asymmetric Auctions
Generalized Vickrey Auctions
Multi-Unit, Sequential, Clock Auctions
Name Index
Subject Index

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