Quantitative Economics with Python

This website presents a set of lectures on quantitative economic modeling, designed and written by Thomas J. Sargent and John Stachurski.

For an overview of the series, see this page

Tools and Techniques

• 1. Geometric Series for Elementary Economics
• 2. Modeling COVID 19
• 3. Linear Algebra
• 4. QR Decomposition
• 5. Complex Numbers and Trigonometry
• 6. Circulant Matrices
• 7. Singular Value Decomposition (SVD)
• 8. Using Newton’s Method to Solve Economic Models

Elementary Statistics

• 9. Elementary Probability with Matrices
• 10. Univariate Time Series with Matrix Algebra
• 11. LLN and CLT
• 12. Two Meanings of Probability
• 13. Multivariate Hypergeometric Distribution
• 14. Multivariate Normal Distribution
• 15. Heavy-Tailed Distributions
• 16. Fault Tree Uncertainties
• 17. Introduction to Artificial Neural Networks
• 18. Randomized Response Surveys
• 19. Expected Utilities of Random Responses

Linear Programming

• 20. Linear Programming
• 21. Optimal Transport
• 22. Von Neumann Growth Model (and a Generalization)

Introduction to Dynamics

• 23. Dynamics in One Dimension
• 24. AR1 Processes
• 25. Finite Markov Chains
• 26. Inventory Dynamics
• 27. Linear State Space Models
• 28. Samuelson Multiplier-Accelerator
• 29. Kesten Processes and Firm Dynamics
• 30. Wealth Distribution Dynamics
• 31. A First Look at the Kalman Filter
• 32. Shortest Paths

Search

• 33. Job Search I: The McCall Search Model
• 34. Job Search II: Search and Separation
• 35. Job Search III: Fitted Value Function Iteration
• 36. Job Search IV: Correlated Wage Offers
• 37. Job Search V: Modeling Career Choice
• 38. Job Search VI: On-the-Job Search
• 39. Job Search VII: A McCall Worker Q-Learns

Consumption, Savings and Capital

• 40. Cass-Koopmans Model
• 41. Cass-Koopmans Competitive Equilibrium
• 42. Cake Eating I: Introduction to Optimal Saving
• 43. Cake Eating II: Numerical Methods
• 44. Optimal Growth I: The Stochastic Optimal Growth Model
• 45. Optimal Growth II: Accelerating the Code with Numba
• 46. Optimal Growth III: Time Iteration
• 47. Optimal Growth IV: The Endogenous Grid Method
• 48. The Income Fluctuation Problem I: Basic Model
• 49. The Income Fluctuation Problem II: Stochastic Returns on Assets

Bayes Law

• 50. Non-Conjugate Priors
• 51. Posterior Distributions for AR(1) Parameters
• 52. Forecasting an AR(1) process

Information

• 53. Job Search VII: Search with Learning
• 54. Likelihood Ratio Processes
• 55. Computing Mean of a Likelihood Ratio Process
• 56. A Problem that Stumped Milton Friedman
• 57. Exchangeability and Bayesian Updating
• 58. Likelihood Ratio Processes and Bayesian Learning
• 59. Incorrect Models
• 60. Bayesian versus Frequentist Decision Rules

LQ Control

• 61. LQ Control: Foundations
• 62. Lagrangian for LQ Control
• 63. Eliminating Cross Products
• 64. The Permanent Income Model
• 65. Permanent Income II: LQ Techniques
• 66. Production Smoothing via Inventories

Multiple Agent Models

• 67. Schelling’s Segregation Model
• 68. A Lake Model of Employment and Unemployment
• 69. Rational Expectations Equilibrium
• 70. Stability in Linear Rational Expectations Models
• 71. Markov Perfect Equilibrium
• 72. Uncertainty Traps
• 73. The Aiyagari Model

Asset Pricing and Finance

• 74. Asset Pricing: Finite State Models
• 75. Competitive Equilibria with Arrow Securities
• 76. Heterogeneous Beliefs and Bubbles

Data and Empirics

• 77. Pandas for Panel Data
• 78. Linear Regression in Python
• 79. Maximum Likelihood Estimation

Auctions

• 80. First-Price and Second-Price Auctions
• 81. Multiple Good Allocation Mechanisms

Other

• 82. Troubleshooting
• 83. References
• 84. Execution Statistics

Previous website

While this new site will receive all future updates, you may still view the old site here for the next month.