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75753-01 - Lecture: Predictions: Theory and Practice (3 CP)
| Semester | spring semester 2025 |
| Course frequency | Irregular |
| Lecturers | Patrick Schmidt (patrick.schmidt@unibas.ch, Assessor) |
| Content | This course introduces the theory and art of predictions. We take a practical perspective on a technical and quantitative topic. The first part focuses on how predictions are used to make better decisions. We'll cover: - The different forms predictions can take - How to utilize predictions for decision-making - How to update beliefs when new information becomes available The second part explains how to gather predictions from experts and interpret forecasts from experts. We'll investigate: - Elicitation Techniques: Methods used to extract predictions from experts - Cognitive Biases: The systematic errors in judgment that affect both experts and non-experts. - Superforecasting: The characteristics and strategies of "super forecasters" The third part covers how to use data to generate statistical predictions - Monte Carlo Simulation - Bayesian Statistics: Apply Bayesian modeling to combine prior knowledge and data - Loss Functions: Understand how loss functions shape predictions - Calibration: Learn to assess the biases of predictions Application: Forecasting competition. This course culminates in a real-world forecasting competition designed to test your predictive skills. Topics can be chosen according to your interest and may include for example - The value of the Swiss Market Index at the end of the semester - The number of goals Xherdan Shaqiri scores for the FC Basel - Will there be peace in Ukraine at the end of the semester? - Will there be cake at the last lecture? - Will the Swiss unemployment rate rise? Each forecast target has to be clearly defined. Students are expected to hand in forecasts. Together we will evaluate the predictions and compare our performance. This assignment will be graded. There will be a written exam. |
| Learning objectives | Students will learn how to forecast in practice; with experts, modeling, or machine learning. They will understand what characterizes a good prediction and know the common biases that emerge in expert forecasting and machine learning respectively. |
| Bibliography | Interesting books: Silver, N. (2012): The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t, Penguin Press. Tetlock, P. E., & Gardner, D. (2016). Superforecasting: The art and science of prediction. Random House. Tversky, A., Kahneman, D., & Slovic, P. (1982). Judgment under uncertainty: Heuristics and biases Technical books: James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statistical learning: with applications in R. Second Edition. Spinger. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995). Bayesian data analysis. Chapman and Hall/CRC. Murphy, K. P. (2012): Machine Learning: A Probabilistic Perspective, MIT Press. O’Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., and Rakow, T. (2006), Uncertain Judgements: Eliciting Expert Probabilities, Chichester, UK: Wiley. Augustin, T., Coolen, F. P., De Cooman, G., & Troffaes, M. C. (Eds.). (2014). Introduction to imprecise probabilities (Vol. 591). John Wiley & Sons. |
| Admission requirements | Prior experience in programming, statistics, and microeconomic theory will be helpful. Otherwise, I can provide material to read up on the basics. Programming examples will be mostly in R, but assignment solutions can be based on any programming language. |
| Course application | Registration: Please enroll in the Online Services (services.unibas.ch); Eucor-Students and mobility students of other Swiss Universities or the FHNW first have to register at the University of Basel BEFORE the start of the course and receive their login data by post (e-mail address of the University of Basel). Processing time up to a week! Detailed information can be found here: https://www.unibas.ch/de/Studium/Mobilitaet.html After successful registration you can enroll for the course in the Online Services (services.unibas.ch). Applies to everyone: Enrolment = Registration for the course and the exam! |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Tuesday | 08.30-10.00 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S14 HG.32 |
Dates
| Modules |
Modul: Fachlich-methodische Ausbildung (PhD subject: Staatswissenschaften) Module: Field Electives in Finance and Money (Master's Studies: Finance and Money) Module: Specific Electives in Data Science and Computational Economics (Master's Studies: Business and Economics) Module: Specific Electives in Economics (Master's Studies: Business and Economics) Module: Specific Electives in Finance, Controlling, Banking (Master's Studies: Business and Economics) Specialization Module: Areas of Specialization in International and/or Monetary Economics (Master's Studies: International and Monetary Economics) |
| Assessment format | record of achievement |
| Assessment details | Students are expected to hand in forecasts. Together we will evaluate the predictions and compare our performance. This assignment will be graded. There will be a written exam. Final grading: assignment (50%) / written exam (50%) |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,1 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Business and Economics , studiendekanat-wwz@unibas.ch |
| Offered by | Faculty of Business and Economics |