A First Course in Quantitative Economics with Python

This lecture series provides an introduction to quantitative economics using Python.

The lectures were designed and written by Thomas J. Sargent and John Stachurski, with extensive help from the rest of the QuantEcon team.

Introduction

• 1. About These Lectures

Economic Data

• 2. Economic Growth Evidence
• 3. Business Cycles
• 4. Income and Wealth Inequality

Essential Tools

• 5. Linear Equations and Matrix Algebra
• 6. Eigenvalues and Eigenvectors
• 7. Introduction to Supply and Demand

Linear Dynamics

• 8. Present Values
• 9. Consumption Smoothing
• 10. Equalizing Difference Model
• 11. Price Level Histories
• 12. A Fiscal Theory of the Price Level
• 13. A Fiscal Theory of Price Level with Adaptive Expectations
• 14. Geometric Series for Elementary Economics

Probability and Distributions

• 15. Distributions and Probabilities
• 16. LLN and CLT
• 17. Monte Carlo and Option Pricing
• 18. Heavy-Tailed Distributions
• 19. Racial Segregation

Nonlinear Dynamics

• 20. The Solow-Swan Growth Model
• 21. Dynamics in One Dimension
• 22. The Cobweb Model
• 23. The Overlapping Generations Model
• 24. Commodity Prices

Stochastic Dynamics

• 25. Markov Chains: Basic Concepts
• 26. Markov Chains: Irreducibility and Ergodicity
• 27. Univariate Time Series with Matrix Algebra

Optimization

• 28. Linear Programming
• 29. Shortest Paths

Modeling in Higher Dimensions

• 30. The Perron-Frobenius Theorem
• 31. Input-Output Models
• 32. A Lake Model of Employment
• 33. Networks

Markets and Competitive Equilibrium

• 34. Supply and Demand with Many Goods
• 35. Market Equilibrium with Heterogeneity

Estimation

• 36. Simple Linear Regression Model
• 37. Maximum Likelihood Estimation

Other

• 38. Troubleshooting
• 39. References
• 40. Execution Statistics