Stachurskii John. Economic Dynamics Theory and Computation

Stachurski John -Economic Dynamics Theory and Computation, The MIT Press -2009, - 392p.
Contents.
Preface xiii.
Common Symbols xvii.
Introduction.
I Introduction to Dynamics.
Introduction to Programming.
Basic Techniques.
Algorithms.
Coding: First Steps.
Modules and Scripts.
Flow Control.
Program Design.
User-Defined Functions.
More Data Types.
Object-Oriented Programming.
Commentary.
Analysis in Metric Space.
A First Look at Metric Space.
Distances and Norms.
Sequences.
Open Sets, Closed Sets.
Further Properties.
Completeness.
Compactness.
vii.
viii Contents.
Optimization, Equivalence.
Fixed Points.
Commentary.
Introduction to Dynamics.
Deterministic Dynamical Systems.
The Basic Model.
Global Stability.
Chaotic Dynamic Systems.
Equivalent Dynamics and Linearization.
Finite State Markov Chains.
Definition.
Marginal Distributions.
Other Identities.
Constructing Joint Distributions.
Stability of Finite State MCs.
Stationary Distributions.
The Dobrushin Coefficient.
Stability.
The Law of Large Numbers.
Commentary.
Further Topics for Finite MCs.
Optimization.
Outline of the Problem.
Value Iteration.
Policy Iteration.
MCs and SRSs.
From MCs to SRSs.
Application: Equilibrium Selection.
The Coupling Method.
Commentary.
Infinite State Space.
First Steps.
Basic Models and Simulation.
Distribution Dynamics.
Density Dynamics.
Stationary Densities: First Pass.
Optimal Growth, Infinite State.
Contents ix.
Optimization.
Fitted Value Iteration.
Policy Iteration.
Stochastic Speculative Price.
The Model.
Numerical Solution.
Equilibria and Optima.
Commentary.
II Advanced Techniques.
Integration.
Measure Theory.
Lebesgue Measure.
Measurable Spaces.
General Measures and Probabilities.
Existence of Measures.
Definition of the Integral.
Integrating Simple Functions.
Measurable Functions.
Integrating Measurable Functions.
Properties of the Integral.
Basic Properties.
Finishing Touches.
The Space L.
Commentary.
Density Markov Chains.
Outline.
Stochastic Density Keels.
Connection with SRSs.
The Markov Operator.
Stability.
The Big Picture.
Dobrushin Revisited.
Drift Conditions.
Applications.
Commentary.
x Contents.
Measure-Theoretic Probability.
Random Variables.
Basic Definitions.
Independence.
Back to Densities.
General State Markov Chains.
Stochastic Keels.
The Fundamental Recursion, Again.
Expectations.
Commentary.
Stochastic Dynamic Programming.
Theory.
Statement of the Problem.
Optimality.
Proofs.
Numerical Methods.
Value Iteration.
Policy Iteration.
Fitted Value Iteration.
Commentary.
Stochastic Dynamics.
Notions of Convergence.
Convergence of Sample Paths.
Strong Convergence of Measures.
Weak Convergence of Measures.
Stability: Analytical Methods.
Stationary Distributions.
Testing for Existence.
The Dobrushin Coefficient, Measure Case.
Application: Credit-Constrained Growth.
Stability: Probabilistic Methods.
Coupling with Regeneration.
Coupling and the Dobrushin Coefficient.
Stability via Monotonicity.
More on Monotonicity.
Further Stability Theory.
Commentary.
Contents xi.
More Stochastic Dynamic Programming.
Monotonicity and Concavity.
Monotonicity.
Concavity and Differentiability.
Optimal Growth Dynamics.
Unbounded Rewards.
Weighted Supremum Norms.
Results and Applications.
Proofs.
Commentary.
III Appendixes.
Real Analysis.
The Nuts and Bolts.
Sets and Logic.
Functions.
Basic Probability.
The Real Numbers.
Real Sequences.
Max, Min, Sup, and Inf.
Functions of a Real Variable.
B Chapter Appendixes.
Bibliography.
Index.