The second edition of a rigorous and example-driven introduction to topics in economic dynamics that emphasizes techniques for modeling dynamic systems.
The second edition of a rigorous and example-driven introduction to topics in economic dynamics that emphasizes techniques for modeling dynamic systems.
This text provides an introduction to the modern theory of economic dynamics, with emphasis on mathematical and computational techniques for modeling dynamic systems. Written to be both rigorous and engaging, the book shows how sound understanding of the underlying theory leads to effective algorithms for solving real-world problems. The material makes extensive use of programming examples to illustrate ideas, bringing to life the abstract concepts in the text. Key topics include algorithms and scientific computing, simulation, Markov models, and dynamic programming. Part I introduces fundamentals and part II covers more advanced material. This second edition has been thoroughly updated, drawing on recent research in the field.
New for the second edition-
"Programming-language agnostic" presentation using pseudocode.
New chapter 1 covering conceptual issues concerning Markov chains such as ergodicity and stability.
New focus in chapter 2 on algorithms and techniques for program design and high-performance computing.
New focus on household problems rather than optimal growth in material on dynamic programming.
Solutions to many exercises, code, and other resources available on a supplementary website.
The second edition of a rigorous and example-driven introduction to topics in economic dynamics that emphasizes techniques for modeling dynamic systems.
The second edition of a rigorous and example-driven introduction to topics in economic dynamics that emphasizes techniques for modeling dynamic systems.
This text provides an introduction to the modern theory of economic dynamics, with emphasis on mathematical and computational techniques for modeling dynamic systems. Written to be both rigorous and engaging, the book shows how sound understanding of the underlying theory leads to effective algorithms for solving real-world problems. The material makes extensive use of programming examples to illustrate ideas, bringing to life the abstract concepts in the text. Key topics include algorithms and scientific computing, simulation, Markov models, and dynamic programming. Part I introduces fundamentals and part II covers more advanced material. This second edition has been thoroughly updated, drawing on recent research in the field.
New for the second edition-
"Programming-language agnostic" presentation using pseudocode.
New chapter 1 covering conceptual issues concerning Markov chains such as ergodicity and stability.
New focus in chapter 2 on algorithms and techniques for program design and high-performance computing.
New focus on household problems rather than optimal growth in material on dynamic programming.
Solutions to many exercises, code, and other resources available on a supplementary website.
Preface xiii
Common Symbols xvii
I Introduction to Dynamics 1
1 Introduction 3
2 Programming 25
3 Analysis in Metric Space 39
4 Introduction to Dynamics 59
5 Further Topics for finite MCs 99
6 Infinite State Space 115
II Advances Techniques 153
7 Integration 155
8 Density Markov Chains 185
9 Measure-Theoretic Probability 209
10 Stochastic Dynamic Programming 227
11 Stochastic Dynamics 247
12 More Stochastic Dynamic Programming 295
III Appendixes 315
A Real Analysis 317
B Chapter Appendixes 339
Bibliography 357
Index 369
John Stachurski is Professor of Economics at Australian National University and cofounder of QuantEcon. He is the author of A Primer in Econometric Theory (MIT Press).
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