Bridging Course in Econometrics
Graduate course, WU Vienna, Department of Economics, 2026
This graduate-level bridging course provides essential econometric foundations for incoming master’s students. The course covers fundamental statistical concepts, probability theory, and introductory econometric methods necessary for advanced graduate coursework. Topics include probability theory, asymptotic theory, parameter estimation and standard econometric models such as the linear model, binary choice, multinomial, etc.
Lecture slides
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| # | Topic | ||
|---|---|---|---|
| 1 | Introduction — what statistics is, and what this course covers | Slides | |
| 2 | Random Variables — distributions, moments, transformations, and limit theorems | Slides | |
| 3 | Multivariate Random Variables — joint laws, independence, dependence, and the multivariate normal | Slides | |
| 4 | Estimation — from a point estimate to an interval: bias and variance, losses, maximum likelihood, GMM, efficiency, confidence intervals, and p-values | Slides | |
| 5 | Hypothesis Testing — errors, power, likelihood ratios, and multiple comparisons | Slides | |
| 6 | Bayesian Inference — priors, posteriors, conjugate and non-conjugate updating, shrinkage (ridge to horseshoe), evidence, and spike-and-slab model selection | Slides | |
| 7 | Econometric Models — the linear model, binary choice, and a tour of count, censored, panel, IV, and quantile models, with R output | Slides | |
| 8 | Statistical Challenges — what breaks when the assumptions do | Slides |
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