Welcome to Computational Economics!

A 400 level undergraduate economics course offered at the Department of Economics at Towson University in Maryland, USA.

Course Instructor: Prof. Juergen Jung

email: jjung@towson.edu

web: https://juejung.github.io/

Contents:

• 1. Course Administration
  • 1.1. Installing Python
  • 1.2. Submitting homework, the midterm and the final via Dropbox
• 2. First Steps in Python
  • 2.1. Simple Calculations
  • 2.2. The Print Function
  • 2.3. Script Files
  • 2.4. iPython Notebook
  • 2.5. Key Concepts and Summary
  • 2.6. Self-Check Questions
• 3. Python Data Structures
  • 3.1. Lists
  • 3.2. Tuples
  • 3.3. Dictionaries
  • 3.4. Pitfalls
  • 3.5. JSON Data Format
  • 3.6. In Class Exercises
• 4. Basic Programming Techniques
  • 4.1. Branching into Alternative Code Blocks
    • 4.1.1. The if statment
    • 4.1.2. Branching with Logical Operators
  • 4.2. For-Loops
    • 4.2.1. Loop to Repeat a Simple Print Function
    • 4.2.2. Loop to Calculate the Sum of Numbers
    • 4.2.3. Loop to Calculate the Product of Numbers
    • 4.2.4. Loop to Print the Elements stored in a List
    • 4.2.5. Loop with Iterator Values Not Starting at Zero
    • 4.2.6. Nested Loops
    • 4.2.7. Looping through the Content of a List
    • 4.2.8. Combining Lists and Looping Through Them
  • 4.3. While Loop
    • 4.3.1. While Loop 1
    • 4.3.2. While Loop 2
  • 4.4. Combining Branching Statements and Loops
  • 4.5. Time Your Work
  • 4.6. Key Concepts and Summary
  • 4.7. Self-Check Questions
• 5. Debugging
  • 5.1. Using Print Statements to Spot Mistakes
  • 5.2. Using the Debugger
  • 5.3. In Class Exercises
• 6. Vectors and Matrices
  • 6.1. Definitions of Vectors
  • 6.2. Simple Calculations with Vectors
  • 6.3. Simple Calculations with Matrices
  • 6.4. Vector Manipulation
    • 6.4.1. Transposing Vectors
    • 6.4.2. Length of Vectors
    • 6.4.3. Adding Two Vectors
    • 6.4.4. Multiplication of Vectors
  • 6.5. Matrix Manipulation
    • 6.5.1. Indexing and Accessing Elements of a Matrix
    • 6.5.2. Transposing Matrices
    • 6.5.3. Adding Matrices
    • 6.5.4. Multiplying Matrices
    • 6.5.5. Multiplying a Matrix with a Vector
  • 6.6. Key Concepts and Summary
  • 6.7. Self-Check Questions
• 7. Plotting using matplotlib
  • 7.1. Plotting Vectors and Arrays
    • 7.1.1. Plotting Simple Vectors
    • 7.1.2. Plotting Two Vectors
    • 7.1.3. Graph 3 Car Types
  • 7.2. Plotting Functions
    • 7.2.1. First Example
    • 7.2.2. Second Example Using subplots()
    • 7.2.3. Plotting Multiple Functions into a Graph
  • 7.3. Subplots
  • 7.4. 3D-Graphs
  • 7.5. 3D-Utility Function Graphs
  • 7.6. Key Concepts and Summary
  • 7.7. Self-Check Questions
• 8. Functions
  • 8.1. A Simple Demonstration Example
  • 8.2. Simple User Defined Function in Separate File
  • 8.3. Advanced Graphing using Loops and Functions
    • 8.3.1. Graphing Functions with two Input Arguments z = f(x,y)
    • 8.3.2. Same Thing but Different Function
  • 8.4. Functions with Default Arguments
  • 8.5. Scope of Variables in Functions
  • 8.6. Scope of Lists and Arrays in Functions
  • 8.7. Key Concepts and Summary
  • 8.8. Self-Check Questions
• 9. Object Oriented Programming
  • 9.1. Introduction
  • 9.2. Inheritance and Subclasses
  • 9.3. Generating Multiple Objects
• 10. Working with Data I: Data Cleaning
  • 10.1. Create Dataframe from Multiple Lists
    • 10.1.1. Every List Holds Information of a Separate Variable
    • 10.1.2. Every List Holds Information of one Unit of Observation
  • 10.2. Read Small Data Set from a Comma Separated (.csv) File
  • 10.3. Some Pandas Tricks
    • 10.3.1. Renaming Variables
    • 10.3.2. Dropping and Adding Columns
    • 10.3.3. Missing Values of NaN’s
    • 10.3.4. Dropping and Adding Rows to a DataFrame
    • 10.3.5. Dropping Rows Based on Conditions
    • 10.3.6. Sorting and Reindexing DataFrames
    • 10.3.7. Merging DataFrames or Adding Columns
    • 10.3.8. Converting Column Types
    • 10.3.9. Replacing Values in a DataFrame Conditional on Criteria
• 11. Working with Data II: Statistics
  • 11.1. Importing Data
  • 11.2. Making Simple Graphs from our Data
    • 11.2.1. Bar Charts
    • 11.2.2. Pie Charts
    • 11.2.3. Histograms
    • 11.2.4. Boxplots
  • 11.3. Summary Statistics
    • 11.3.1. Measures of Central Tendency
    • 11.3.2. Measures of Dispersion
    • 11.3.3. Measures of Relative Standing
    • 11.3.4. Data Aggregation or Summary Statistics by Subgroup
  • 11.4. Measures of Linear Relationship
    • 11.4.1. Covariance
    • 11.4.2. Correlation Coefficient
    • 11.4.3. Regression Line: Example 1
    • 11.4.4. Regression Line: Example 2
  • 11.5. Measures of Non-Linear Relationship
    • 11.5.1. Logit
    • 11.5.2. Probit
  • 11.6. Tutorials
• 12. Working with Data III: Case Study
  • 12.1. Importing Data
  • 12.2. Plotting Cases of Infections
  • 12.3. Plotting Cases of Infections on a Map
  • 12.4. Plotting Time Series of Cases
  • 12.5. Plotting Time Series of Corona Virus Deaths
  • 12.6. Key Concepts and Summary
  • 12.7. Self-Check Questions
• 13. Working with Data from the Web I
  • 13.1. Beautiful Soup Library
  • 13.2. Scraping YouTube
    • 13.2.1. Introduction
    • 13.2.2. Extracting Information from html Code
    • 13.2.3. Storing Information in a DataFrame
    • 13.2.4. Extract the View Count for the First Video
    • 13.2.5. Extract the Likes and Dislikes for the First Video
    • 13.2.6. Extract Remaining Video Info Using Loop
  • 13.3. How to Deal with Errors
  • 13.4. More Tutorials
• 14. Working with Data from the Web II
  • 14.1. Intro to APIs
  • 14.2. Yelp-API
  • 14.3. Economic Data from the Federal Reserve
  • 14.4. More Tutorials
• 15. Regular Expressions
  • 15.1. First Steps
  • 15.2. Example 1: Matching Phone Numbers
  • 15.3. Example 2: Matching Email Addresses
  • 15.4. Tutorials
  • 15.5. References
• 16. Random Numbers
  • 16.1. Drawing a Random Number
  • 16.2. Uniformly Distributed Random Numbers
  • 16.3. Normally distributed random numbers
  • 16.4. T-Distributed Random Variable
  • 16.5. Drawing Random Integer Values
  • 16.6. Generating actions based on probabilities
  • 16.7. Drawing Objects with Random Values
  • 16.8. Key Concepts and Summary
  • 16.9. Self-Check Questions
• 17. Random Numbers II: An Infectious Disease Simulation
  • 17.1. Defining a Class Object of a Person
  • 17.2. Defining Functions for Plotting and Moving Individuals
  • 17.3. Running the Simulation
  • 17.4. S.I.R. Model - A More Complete Model of Infectious Disease
  • 17.5. Key Concepts and Summary
  • 17.6. Self-Check Questions
• 18. Root Finding
  • 18.1. First Steps
  • 18.2. Newton-Raphson
  • 18.3. Secant Method
  • 18.4. Bisection
  • 18.5. Using Built in Unit-Root Function
• 19. Optimization
  • 19.1. Univariate Function Optimization
  • 19.2. Optimization Methods
    • 19.2.1. Newton’s Method
    • 19.2.2. Golden Section Method
    • 19.2.3. Use Built-In Method to Maximize Function
  • 19.3. Finding the Root of a Function using Minimization
  • 19.4. Multivariate Optimization
    • 19.4.1. Function
    • 19.4.2. Multivariate Newton Method
• 20. Constrained Optimization
  • 20.1. The Cake Eating Problem
  • 20.2. Solution
  • 20.3. Formally
  • 20.4. Analytical Solution
  • 20.5. Solving the Problem
  • 20.6. Plotting the Solution
  • 20.7. Numerical Solution in Python
• 21. A Simple OLG Model
  • 21.1. Formal Definition of the Model
  • 21.2. Equilibrium Definition
  • 21.3. Functional Forms and Solutions
  • 21.4. Method 1: Substituting Everything
  • 21.5. Method 2: Gauss-Seidl Algorithm
  • 21.6. Tax Policy Simulation
  • 21.7. Key Concepts and Summary
  • 21.8. Self-Check Questions
• 22. An OLG Model with Labor-Leisure Choice
  • 22.1. Preferences
  • 22.2. Technology
  • 22.3. Government
  • 22.4. Household Problem
  • 22.5. Equilibrium Definition
  • 22.6. Functional Forms and Solutions
  • 22.7. Substitute in Preferences
  • 22.8. Solve for Optimal \(s^*\) and \(l^*\)
• 23. Growth Models
  • 23.1. Preferences
  • 23.2. Technology
  • 23.3. Government
  • 23.4. Household problem
  • 23.5. Equilibrium definition
• 24. Machine Learning I: Introduction to Machine Learning
  • 24.1. Different Types of Machine Learning Algorithms
  • 24.2. A First Regression Model Example
    • 24.2.1. Downloading the Data
    • 24.2.2. Inspecting the Data
    • 24.2.3. Splitting the Data
    • 24.2.4. Visualize data to gain insight
    • 24.2.5. Prepare data
    • 24.2.6. Training a Regression Model
    • 24.2.7. Fine Tune Model
  • 24.3. Key Concepts and Summary
  • 24.4. Self-Check Questions
• 25. Machine Learning II: Categorization Algorithm
  • 25.1. A Simple Classification Model
    • 25.1.1. Downloading the Data
    • 25.1.2. Training the Model
    • 25.1.3. Making Predictions
    • 25.1.4. Precision Recall Trade-off
    • 25.1.5. ROC curves
  • 25.2. Key Concepts and Summary
  • 25.3. Self-Check Questions
• 26. Assignments
  • 26.1. Homework
  • 26.2. Module 1
  • 26.3. Module 2
  • 26.4. Midterm 1
  • 26.5. Midterm 2

Indices and tables

• Index

• Module Index

• Search Page