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)

Elementary Statistics

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

Linear Programming

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

Introduction to Dynamics

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

Search

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

Consumption, Savings and Capital

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

Bayes Law

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

Information

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

LQ Control

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

Multiple Agent Models

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

Asset Pricing and Finance

• 71. Asset Pricing: Finite State Models
• 72. Competitive Equilibria with Arrow Securities
• 73. Heterogeneous Beliefs and Bubbles

Data and Empirics

• 74. Pandas for Panel Data
• 75. Linear Regression in Python
• 76. Maximum Likelihood Estimation

Auctions

• 77. First-Price and Second-Price Auctions
• 78. Multiple Good Allocation Mechanisms

Other

• 79. Troubleshooting
• 80. References
• 81. 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.