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| Semester | spring semester 2024 |
| Course frequency | Every spring sem. |
| Lecturers |
Simon Beyeler (simon.beyeler@unibas.ch)
Sylvia Kaufmann (sylvia.kaufmann@unibas.ch, Assessor) |
| Content | The lecture gives an introduction to Bayesian econometrics, with a particular focus on time series analysis, from univariate to multivariate high-dimensional. The primary goal in Bayesian inference is to derive the posterior distribution of an object of interest, being usually parameters or some latent variables. Therefore, in a first part we define the basic components specifying the Bayesian setup, the prior and the likelihood, and discuss principles of posterior updating. As for most econometric models the posterior distribution is not of a known standard form nor available in analytical form, the posterior distribution is approximated or estimated by sampling methods. We introduce two generic samplers based on Markov chain Monte Carlo (MCMC) simulation methods to estimate the posterior distribution: Metropolis-Hastings and Gibbs sampling. Bayesian inference inherently lends itself to a probabilistic interpretation or discussion of model estimates. To quantify uncertainty, we derive procedures to obtain credible intervals, for parameters as well as (non)linear transformations of parameters. Finally, we also discuss approaches to perform model choice or (forecast) evaluation, like MCMC-based estimation of the marginal likelihood or $K$-fold cross-validation. The Bayesian approach circumvents estimation difficulties when either data is scarce or high-dimensional. To deal with these issues, we discuss ways of specifying informative prior distributions and prior distributions that induce shrinkage into parameters. In a last part, we introduce latent variables which allow extending models to regime-switching parameters or extracting a small number of common factors from high-dimensional datasets. The lecture also includes the analytical discussion of time series models. We derive properties of the time series process, discuss stationarity and invertibility conditions, derive conditional and unconditional moments. As single parameters are not of prime interest, tools like impulse responses and variance decomposition are used to interpret multivariate time series models. We discuss various strategies of structural identification. The lecture includes exercise sessions with applications in time series modelling. |
| Learning objectives | - Understand the differences between frequentist estimation and Bayesian inference. - Know the principles of Bayesian inference, Bayesian updating - Know and apply generic Markov chain Monte Carlo sampler: Metropolis-Hastings and Gibbs - Evaluate the posterior distribution: Determine posterior moments and uncertainty (credible intervals) - Know basic measures or procedures for model choice - Derive analytical characteristics of a time series model - Know and apply basic tools of model interpretation: Structural identification, impulse responses, variance decomposition - Implement estimation and various structural identification - Estimate basic latent variable models. - Basic knowledg - Analyse and derive the properties of multivariate time series models. - Perform model specification/comparison; understand and apply tools to interpret model estimates. - Understand the differences between frequentist estimation and Bayesian inference. - Implement estimation and various structural identification procedures, quantify uncertainty. - Estimate basic latent variable models. - Basic knowledge of forecasting procedures, forecast evaluation. |
| Bibliography | Gelman A., Carlin J.B., Stern H.S. and Rubin, D.R. (1995), Bayesian Data Analysis, Chapman and Hall, London. Greenberg Edward, 2013, Introduction to Bayesian Econometrics, Cambridge University Press, Cambridge UK. Lütkepohl Helmut, 2005, New Introduction to Multiple Time Series Analysis, Springer. Neusser Klaus, 2016, Time Series Econometrics, Springer International Publishing AG Switzerland. Popular scientific: Bertsch Mcgrayne Sharon (2011), The theory that would not die: how bayes' rule cracked the enigma code, hunted down russian submarines, and emerged from two centuries of controversy, Yale University Press, New Haven & London.. |
| Comments | The lecture will take place onsite. |
| Weblink | Weblink |
| Admission requirements | Completed BA (preferably in economics). Econometrics MA level: Knowledge in regression analysis, univariate time series analysis (advantageous). Knowledge in econometrics or programming software (like e.g. EViews, matlab, R) |
| 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 |
|---|---|---|---|
| 14-täglich | Wednesday | 14.15-17.45 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Date | Time | Room |
|---|---|---|
| Wednesday 06.03.2024 | 14.15-17.45 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Wednesday 20.03.2024 | 14.15-17.45 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Wednesday 27.03.2024 | 14.15-17.45 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Wednesday 10.04.2024 | 14.15-17.45 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S16 HG.39 |
| Wednesday 17.04.2024 | 14.15-17.45 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Wednesday 08.05.2024 | 14.15-17.45 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Wednesday 22.05.2024 | 14.15-17.45 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
| Modules |
Modul: Fachlich-methodische Ausbildung (PhD subject: Staatswissenschaften) Modul: Fachlich-methodische Weiterbildung (Doctoral Studies - Faculty of Business and Economics (start of studies before 01.02.2024)) Module: Field Electives in Economics and Public Policy (Master's Studies: Economics and Public Policy) Module: Field Electives in Finance and Money (Master's Studies: Finance and Money) Module: Finance Field: Monetary Economics and Macrofinance (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: Statistics and Computational Science (Master's Studies: Actuarial Science) 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 | 40% Two assignments (team work of 3-5 persons) 60% Written exam (open book): 04.06.24; 10:15-11:15. You can still withdraw from the examination by submitting a completed, signed form to our office from march 26 until april 5 / 12:00 o’clock. The deregistration form and the mail address can be found on the homepage of the Dean of Studies Office: https://wwz.unibas.ch/en/studies/examinations/de-/registration-of-examinations/ Prior to march 25, please deregister only in the Online Services. |
| 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 |