Dynamic Economics Quantitative Methods and Applications

ISBN-10: 0262012014
ISBN-13: 9780262012010
Edition: 2003
List price: $55.00 Buy it from $32.36
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Description: This book is an effective, concise text for students and researchers that combines the tools of dynamic programming with numerical techniques and simulation-based econometric methods. Doing so, it bridges the traditional gap between theoretical and  More...

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Book details

List price: $55.00
Copyright year: 2003
Publisher: MIT Press
Publication date: 8/29/2003
Binding: Hardcover
Pages: 293
Size: 6.00" wide x 9.00" long x 1.00" tall
Weight: 1.342
Language: English

This book is an effective, concise text for students and researchers that combines the tools of dynamic programming with numerical techniques and simulation-based econometric methods. Doing so, it bridges the traditional gap between theoretical and empirical research and offers an integrated framework for studying applied problems in macroeconomics and microeconomics. In part I the authors first review the formal theory of dynamic optimization; they then present the numerical tools and econometric techniques necessary to evaluate the theoretical models. In language accessible to a reader with a limited background in econometrics, they explain most of the methods used in applied dynamic research today, from the estimation of probability in a coin flip to a complicated nonlinear stochastic structural model. These econometric techniques provide the final link between the dynamic programming problem and data. Part II is devoted to the application of dynamic programming to specific areas of applied economics, including the study of business cycles, consumption, and investment behavior. In each instance the authors present the specific optimization problem as a dynamic programming problem, characterize the optimal policy functions, estimate the parameters, and use models for policy evaluation. The original contribution of Dynamic Economics: Quantitative Methods and Applicationslies in the integrated approach to the empirical application of dynamic optimization programming models. This integration shows that empirical applications actually complement the underlying theory of optimization, while dynamic programming problems provide needed structure for estimation and policy evaluation.

J�r�me Adda is a Lecturer in the Department of Economics at University College, London, and a Research Associate at the Institute of Fiscal Studies.

Russell Cooper is Professor in the Department of Economics at the University of Texas, Austin. He was formerly affiliated with Boston University and was a Visiting Scholar in the Research Department of the Federal Reserve Bank of Minneapolis.

Overview
Theory
Theory of Dynamic Programming
Overview
Indirect Utility
Consumers
Firms
Dynamic Optimization: A Cake-Eating Example
Direct Attack
Dynamic Programming Approach
Some Extensions of the Cake-Eating Problem
Infinite Horizon
Taste Shocks
Discrete Choice
General Formulation
Nonstochastic Case
Stochastic Dynamic Programming
Conclusion
Numerical Analysis
Overview
Stochastic Cake-Eating Problem
Value Function Iterations
Policy Function Iterations
Projection Methods
Stochastic Discrete Cake-Eating Problem
Value Function Iterations
Extensions and Conclusion
Larger State Spaces
Appendix: Additional Numerical Tools
Interpolation Methods
Numerical Integration
How to Simulate the Model
Econometrics
Overview
Some Illustrative Examples
Coin Flipping
Supply and Demand Revisited
Estimation Methods and Asymptotic Properties
Generalized Method of Moments
Maximum Likelihood
Simulation-Based Methods
Conclusion
Applications
Stochastic Growth
Overview
Nonstochastic Growth Model
An Example
Numerical Analysis
Stochastic Growth Model
Environment
Bellman's Equation
Solution Methods
Decentralization
A Stochastic Growth Model with Endogenous Labor Supply
Planner's Dynamic Programming Problem
Numerical Analysis
Confronting the Data
Moments
GMM
Indirect Inference
Maximum Likelihood Estimation
Some Extensions
Technological Complementarities
Multiple Sectors
Taste Shocks
Taxes
Conclusion
Consumption
Overview and Motivation
Two-Period Problem
Basic Problem
Stochastic Income
Portfolio Choice
Borrowing Restrictions
Infinite Horizon Formulation: Theory and Empirical Evidence
Bellman's Equation for the Infinite Horizon Problem
Stochastic Income
Stochastic Returns: Portfolio Choice
Endogenous Labor Supply
Borrowing Constraints
Consumption over the Life Cycle
Conclusion
Durable Consumption
Motivation
Permanent Income Hypothesis Model of Durable Expenditures
Theory
Estimation of a Quadratic Utility Specification
Quadratic Adjustment Costs
Nonconvex Adjustment Costs
General Setting
Irreversibility and Durable Purchases
A Dynamic Discrete Choice Model
Investment
Overview and Motivation
General Problem
No Adjustment Costs
Convex Adjustment Costs
Q Theory: Models
Q Theory: Evidence
Euler Equation Estimation
Borrowing Restrictions
Nonconvex Adjustment: Theory
Nonconvex Adjustment Costs
Irreversibility
Estimation of a Rich Model of Adjustment Costs
General Model
Maximum Likelihood Estimation
Conclusion
Dynamics of Employment Adjustment
Motivation
General Model of Dynamic Labor Demand
Quadratic Adjustment Costs
Richer Models of Adjustment
Piecewise Linear Adjustment Costs
Nonconvex Adjustment Costs
Asymmetries
The Gap Approach
Partial Adjustment Model
Measuring the Target and the Gap
Estimation of a Rich Model of Adjustment Costs
Conclusion
Future Developments
Overview and Motivation
Price Setting
Optimization Problem
Evidence on Magazine Prices
Aggregate Implications
Optimal Inventory Policy
Inventories and the Production-Smoothing Model
Prices and Inventory Adjustment
Capital and Labor
Technological Complementarities: Equilibrium Analysis
Search Models
A Simple Labor Search Model
Estimation of the Labor Search Model
Extensions
Conclusion
Bibliography
Index

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