A First Course in Quantitative Economics with Python

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

Introduction

• 1. About These Lectures

Economic Data

• 2. Long-Run Growth
• 3. Business Cycles
• 4. Price Level Histories
• 5. Inflation During French Revolution
• 6. Income and Wealth Inequality

Foundations

• 7. Introduction to Supply and Demand
• 8. Linear Equations and Matrix Algebra
• 9. Complex Numbers and Trigonometry
• 10. Geometric Series for Elementary Economics

Linear Dynamics: Finite Horizons

• 11. Present Values
• 12. Consumption Smoothing
• 13. Equalizing Difference Model
• 14. A Monetarist Theory of Price Levels
• 15. Monetarist Theory of Price Levels with Adaptive Expectations

Linear Dynamics: Infinite Horizons

• 16. Eigenvalues and Eigenvectors
• 17. Computing Square Roots

Probability and Distributions

• 18. Distributions and Probabilities
• 19. LLN and CLT
• 20. Monte Carlo and Option Pricing
• 21. Heavy-Tailed Distributions
• 22. Racial Segregation

Nonlinear Dynamics

• 23. Dynamics in One Dimension
• 24. The Solow-Swan Growth Model
• 25. The Cobweb Model
• 26. The Overlapping Generations Model
• 27. Commodity Prices

Monetary-Fiscal Policy Interactions

• 28. Money Financed Government Deficits and Price Levels
• 29. Some Unpleasant Monetarist Arithmetic
• 30. Inflation Rate Laffer Curves
• 31. Laffer Curves with Adaptive Expectations

Stochastic Dynamics

• 32. AR(1) Processes
• 33. Markov Chains: Basic Concepts
• 34. Markov Chains: Irreducibility and Ergodicity
• 35. Univariate Time Series with Matrix Algebra

Optimization

• 36. Linear Programming
• 37. Shortest Paths

Modeling in Higher Dimensions

• 38. The Perron-Frobenius Theorem
• 39. Input-Output Models
• 40. A Lake Model of Employment
• 41. Networks

Markets and Competitive Equilibrium

• 42. Supply and Demand with Many Goods
• 43. Market Equilibrium with Heterogeneity

Estimation

• 44. Simple Linear Regression Model
• 45. Maximum Likelihood Estimation

Other

• 46. Troubleshooting
• 47. References
• 48. Execution Statistics