Statistics for Computational Economics

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

Elementary Statistics

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

Information & Bayesian Statistics

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

Applications of Statistics

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

Other

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