Statistics for Computational Economics

This website presents a set of lectures on statistics for computational economics.

Elementary Statistics

• 1. Pandas for Panel Data
• 2. Linear Regression in Python
• 3. Maximum Likelihood Estimation
• 4. Elementary Probability with Matrices
• 5. LLN and CLT
• 6. Multivariate Normal Distribution

Information & Bayesian Statistics

• 7. Two Meanings of Probability
• 8. Non-Conjugate Priors
• 9. Posterior Distributions for AR(1) Parameters
• 10. Forecasting an AR(1) Process
• 11. Likelihood Ratio Processes
• 12. Computing Mean of a Likelihood Ratio Process
• 13. A Problem that Stumped Milton Friedman
• 14. Exchangeability and Bayesian Updating
• 15. Likelihood Ratio Processes and Bayesian Learning
• 16. Incorrect Models
• 17. Bayesian versus Frequentist Decision Rules

Applications of Statistics

• 18. Multivariate Hypergeometric Distribution
• 19. Fault Tree Uncertainties
• 20. Introduction to Artificial Neural Networks
• 21. Randomized Response Surveys
• 22. Expected Utilities of Random Responses

Other

• 23. Troubleshooting
• 24. References
• 25. Execution Statistics