Lectures on Computational Economics

This is a detailed lecture series on computational economics. It covers topics from the text Economic Dynamics: Theory and Computation but with less maths and more emphasis on programming techniques. It can be read independently and includes many solved exercises. The main thing you need to complete the lectures is the desire to be a good programmer. Feel free to contact me at john.stachurski@gmail.com with comments.

Contents:

• Introduction
  • Why CompEcon?
  • What Does this Course Teach?
  • Which Language to Choose?
  • Operating Systems
• First Steps
  • Notes on installation
  • The Interpreter as a Calculator
  • Variables
  • Strings
  • More Sequence Operations
  • Methods
  • Built-in Functions
  • List Comprehensions
  • Problems
• Running Programs with Spyder
  • Choosing an IDE
  • Using Spyder
  • Problems
• Script Files via Text Editors
  • Text Editing
  • Using a Text Editor to Write a Program
  • Running the Script
• Modules
  • The import Statement
  • Other Modules
  • Problems
• Expressions and Conditional Execution
  • Epressions, True and False
  • If/Else
  • Else and Elif
  • General Expressions
  • Boolean Arithmetic
• Loops
  • For loops
  • While loop
• Native Data Types
  • Numeric data types
  • Strings and String Formatting
  • Dictionaries
  • Exercises
• Built-In Functions
  • File I/O
  • Other Built-Ins
  • Exercises
• Iterators
  • Definitions
  • Using Iterators
  • Application: Web Data
• Functions
  • Enhancing Clarity with Functions
  • Defining and Calling Functions
  • Flow
  • Arguments to functions
  • Return Values
  • Doc Strings
  • Exercises
  • Solutions
• User-Defined Functions, Continued
  • One-Line Functions: ‘lambda’
  • Keyword Arguments
  • Recursive Function Calls
• Basic Plotting with Matplotlib
  • Basic Plots
  • Histograms
  • Exercises
• Application: Shortest Paths and Dynamic Programming
  • Outline of the Problem
  • Finding Least-Cost Paths
  • Solving for \(J\)
• Application: Finite State Optimal Growth
  • Outline of the Problem
  • The Bellman Equation
• Namespaces
  • Namespaces and Modules
• Object-Oriented Programming in Python
  • Overview
  • Examples
  • User-Defined ADTs
  • Exercise
• Schelling’s Segregation Model
  • Outline of the Model
  • Modeling
  • My Results
  • Exercise
  • Solution
• NumPy
  • Introduction to NumPy
  • NumPy Arrays
  • Algebraic Operations in NumPy
  • Array Methods
  • Problems
• More NumPy
  • Linear Algebra
  • Random Sampling
  • Other Goodies
  • A Note on Pylab and Matplotlib
  • Exercises
• Application: Finite Markov Chains
  • Definitions
  • Simulation
  • Stationary Distributions
• Stochastic Recursive Sequences
  • The Model
  • Simulation and Time Series
  • Marginal Distributions
• SciPy
  • SciPy versus NumPy
  • Statistics
  • Roots and Fixed Points
  • Optimization
  • Integration
  • Linear Algebra
• Function Approximation
  • The Problem with Finite Memory
  • Step Functions
  • Linear Interpolation
• Continuous State Dynamic Programming
  • An Optimal Growth Model
  • Dynamic Programming
• Continuous State DP Continued
• Commodity Pricing Model
  • Outline of the Model
  • Computing the Solution
• Name Resolution
  • Global and Local Namespaces
  • The __builtins__ Namespace
  • Name resolution
• Handling Exceptions
  • Errors
  • Problems
• Generators
  • Generator Expressions
  • Generator Functions
  • Advantages of Iterators
  • Exercises