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Economic Dynamics, second edition: Theory and Computation [Pehme köide]

  • Formaat: Paperback / softback, 400 pages, kõrgus x laius: 229x178 mm
  • Ilmumisaeg: 16-Aug-2022
  • Kirjastus: MIT Press
  • ISBN-10: 0262544776
  • ISBN-13: 9780262544771
Teised raamatud teemal:
Economic Dynamics, second edition: Theory and ComputationSuurem pilt
  • Formaat: Paperback / softback, 400 pages, kõrgus x laius: 229x178 mm
  • Ilmumisaeg: 16-Aug-2022
  • Kirjastus: MIT Press
  • ISBN-10: 0262544776
  • ISBN-13: 9780262544771
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780262544771.html
Märksõnad:
"A supplement for graduate-level courses on computational and quantitative macro, economic dynamics"--

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(152)
1 Introduction
3(22)
1.1 Stochastic Dynamics
4(13)
1.1.1 Markov Dynamics
4(4)
1.1.2 Interacting Particle Systems
8(3)
1.1.3 A Model of Segregation
11(3)
1.1.4 A Markov Perspective
14(3)
1.2 Where to From Here?
17(5)
1.2.1 General State Space
17(4)
1.2.2 Forward-Looking Agents
21(1)
1.3 Commentary
22(3)
2 Programming
25(14)
2.1 Algorithms
25(5)
2.1.1 Iteration and Flow Control
26(3)
2.1.2 Application: Bisection
29(1)
2.2 Program Design
30(9)
2.2.1 User-Defined Functions
30(4)
2.2.2 Object-Oriented Programming
34(3)
2.2.3 High Performance Computing
37(2)
3 Analysis in Metric Space
39(20)
3.1 A First Look at Metric Space
39(9)
3.1.1 Distances and Norms
40(2)
3.1.2 Sequences
42(3)
3.1.3 Open Sets, Closed Sets
45(3)
3.2 Further Properties
48(10)
3.2.1 Completeness
48(3)
3.2.2 Compactness
51(1)
3.2.3 Optimization, Equivalence
52(3)
3.2.4 Fixed Points
55(3)
3.3 Commentary
58(1)
4 Introduction to Dynamics
59(40)
4.1 Deterministic Dynamical Systems
59(12)
4.1.1 The Basic Model
59(4)
4.1.2 Global Stability
63(3)
4.1.3 Chaotic Dynamic Systems
66(2)
4.1.4 Equivalent Dynamics and Linearization
68(3)
4.2 Finite State Markov Chains
71(13)
4.2.1 Definition
71(3)
4.2.2 From MCs to SRSs
74(3)
4.2.3 Marginal Distributions
77(2)
4.2.4 Other Identities
79(3)
4.2.5 Constructing Joint Distributions
82(2)
4.3 Stability of Finite State MCs
84(13)
4.3.1 Stationary Distributions
85(4)
4.3.2 The Dobrushin Coefficient
89(2)
4.3.3 Stability
91(4)
4.3.4 The Law of Large Numbers
95(2)
4.4 Commentary
97(2)
5 Further Topics for Finite MCs
99(16)
5.1 Optimization
99(8)
5.1.1 Outline of the Problem
99(3)
5.1.2 Value Iteration
102(4)
5.1.3 Policy Iteration
106(1)
5.2 MCs and SRSs
107(6)
5.2.1 Application: Equilibrium Selection
107(3)
5.2.2 The Coupling Method
110(3)
5.3 Commentary
113(2)
6 Infinite State Space
115(38)
6.1 First Steps
115(16)
6.1.1 Basic Models and Simulation
115(5)
6.1.2 Distribution Dynamics
120(2)
6.1.3 Density Dynamics
122(6)
6.1.4 Stationary Densities: First Pass
128(3)
6.2 Optimal Savings, Infinite State
131(9)
6.2.1 Optimization
131(2)
6.2.2 Fitted Value Iteration
133(5)
6.2.3 Policy Iteration
138(2)
6.3 Stochastic Speculative Price
140(9)
6.3.1 The Model
140(5)
6.3.2 Numerical Solution
145(2)
6.3.3 Equilibria and Optima
147(2)
6.4 Commentary
149(4)
II Advanced Techniques
153(162)
7 Integration
155(30)
7.1 Measure Theory
155(12)
7.1.1 Lebesgue Measure
155(4)
7.1.2 Measurable Spaces
159(3)
7.1.3 General Measures and Probabilities
162(2)
7.1.4 Existence of Measures
164(3)
7.2 Definition of the Integral
167(8)
7.2.1 Integrating Simple Functions
167(3)
7.2.2 Measurable Functions
170(3)
7.2.3 Integrating Measurable Functions
173(2)
7.3 Properties of the Integral
175(8)
7.3.1 Basic Properties
175(2)
7.3.2 Finishing Touches
177(3)
7.3.3 The Space La
180(3)
7.4 Commentary
183(2)
8 Density Markov Chains
185(24)
8.1 Outline
185(9)
8.1.1 Stochastic Density Kernels
186(1)
8.1.2 Connection with SRSs
187(6)
8.1.3 The Markov Operator
193(1)
8.2 Stability
194(14)
8.2.1 The Big Picture
195(3)
8.2.2 Dobrushin Revisited
198(4)
8.2.3 Drift Conditions
202(3)
8.2.4 Applications
205(3)
8.3 Commentary
208(1)
9 Measure-Theoretic Probability
209(18)
9.1 Random Variables
209(7)
9.1.1 Basic Definitions
209(4)
9.1.2 Independence
213(1)
9.1.3 Back to Densities
214(2)
9.2 General State Markov Chains
216(9)
9.2.1 Stochastic Kernels
216(5)
9.2.2 The Fundamental Recursion, Again
221(2)
9.2.3 Expectations
223(2)
9.3 Commentary
225(2)
10 Stochastic Dynamic Programming
227(20)
10.1 Theory
227(9)
10.1.1 Statement of the Problem
227(3)
10.1.2 Optimality
230(4)
10.1.3 Proofs
234(2)
10.2 Numerical Methods
236(8)
10.2.1 Value Iteration
237(2)
10.2.2 Policy Iteration
239(3)
10.2.3 Fitted Value Iteration
242(2)
10.3 Commentary
244(3)
11 Stochastic Dynamics
247(48)
11.1 Notions of Convergence
247(10)
11.1.1 Convergence of Sample Paths
247(5)
11.1.2 Strong Convergence of Measures
252(2)
11.1.3 Weak Convergence of Measures
254(3)
11.2 Stability: Analytical Methods
257(14)
11.2.1 Stationary Distributions
257(3)
11.2.2 Testing for Existence
260(3)
11.2.3 The Dobrushin Coefficient, Measure Case
263(3)
11.2.4 Application: Credit-Constrained Growth
266(5)
11.3 Stability: Probabilistic Methods
271(22)
11.3.1 Coupling with Regeneration
272(4)
11.3.2 Coupling and the Dobrushin Coefficient
276(3)
11.3.3 Stability via Monotonicity
279(4)
11.3.4 More on Monotonicity
283(4)
11.3.5 Further Stability Theory
287(6)
11.4 Commentary
293(2)
12 More Stochastic Dynamic Programming
295(20)
12.1 Monotonicity and Concavity
295(11)
12.1.1 Monotonicity
295(4)
12.1.2 Concavity and Differentiability
299(3)
12.1.3 Wealth Dynamics
302(4)
12.2 Unbounded Rewards
306(7)
12.2.1 Weighted Supremum Norms
306(2)
12.2.2 Results and Applications
308(3)
12.2.3 Proofs
311(2)
12.3 Commentary
313(2)
III Appendixes
315(42)
A Real Analysis
317(1)
A.1 The Nuts and Bolts
317(10)
A.1.1 Sets and Logic
317(3)
A.1.2 Functions
320(4)
A.1.3 Basic Probability
324(3)
A.2 The Real Numbers
327(12)
A.2.1 Real Sequences
327(4)
A.2.2 Max, Min, Sup, and Inf
331(3)
A.2.3 Functions of a Real Variable
334(5)
B
Chapter Appendixes
339(1)
B.1 Appendix to
Chapter 3
339(3)
B.2 Appendix to
Chapter 4
342(2)
B.3 Appendix to
Chapter 6
344(1)
B.4 Appendix to
Chapter 8
345(2)
B.5 Appendix to
Chapter 10
347(2)
B.6 Appendix to
Chapter 11
349(1)
B.7 Appendix to
Chapter 12
350(7)
Bibliography 357(12)
Index 369