The course focuses on econometric methods used with macroeconomic data. The lecture consists of the following main building blocks:

• (Recap: Mathematical tools, probability and statistics, classical econometrics)
• Univariate and multivariate time series analysis
• Introduction to Bayesian econometrics
• Structural identification, inference and dynamic causal effects
• Forecasting and predictive inference

We begin by discussing dynamic models in a (macro)economic context and proceed with univariate time series processes such as autoregressive moving average (ARMA) models. Turning from theoretical considerations towards empirical implementations, students are introduced to Bayesian econometric methods for inference. This also involves applied work with the statistical software R.

Subsequently we generalize the preceding concepts to the multivariate case, and will in particular discuss vector autoregressions (VARs). The topics we cover in this context include identification of structural shocks, and related tools such as impulse response functions (IRFs). For the latter we will discuss both VAR-based and local projection (LP) approaches to inference. Finally, we will discuss forecasting and how to obtain statistical measures of predictive accuracy.

By the end of the course, students are expected to have acquired a good understanding of how to analyze univariate and multivariate time series, and how to apply this knowledge in a macroeconomic context. This implies that they will be able to follow and comment on state-of-the-art macroeconomic and macroeconometric academic papers, and derive recommendations for decision- and policymakers from quantitative econometric models.

This course is designed for students interested in working at policy, research or financial institutions, and covers the most important econometric tools used in empirical macroeconomics. Rather than focusing narrowly on the application of econometric tools in macroeconomics, the course aims to provide a deeper understanding of related methods, their proper use, and potential limitations. 

The methods discussed in the course, such as univariate and multivariate time series models, are used heavily in an academic context, in central banks, and policy and financial institutions. By the end of the course, students should be able to conduct their own research projects and empirical analyses using time series analysis, and assess the quality of papers.

Attendance is mandatory for this course, two absences will be tolerated.

Course materials are self-contained and will be made available to participants in the form of slides and computer code. The slides are partly based on the following books:

• Chan, J., Koop, G., Poirier, D.J. and Tobias, J.L.: "Bayesian Econometric Methods" (Cambridge University Press)
• Hamilton, J.D.: "Time Series Analysis" (Princeton University Press).

The lecture consists of two main blocks. First, we will discuss the topics listed in this syllabus based on the course materials (slides and codes) mentioned above. Second, you are asked to present research papers in groups.

The groups (max. 5 students) are expected to scrutinize the respective paper in depth (objectives, relevant assumptions, model framework, and results) and provide (1) a detailed discussion, as well as (2) potential comments/questions/suggestions. There will be sufficient time for thorough discussions in class. 

The course grade will be based on the following components:

• Midterm (20 points)
• Final exam (40 points)
• Assignments (20 points total, 10 points each), in groups
• Presentation (20 points), in groups

The groups consist of a maximum of 5 students.

To pass the course, a positive final exam score (50% or higher of total points, i.e. 22.5 points) is required. The relevant material for the exam is defined by what has been taught in the course. The grading scheme is:

• Excellent (1): [89, 100] points
• Good (2): [78, 89) points
• Satisfactory (3): [60, 78) points
• Sufficient (4): [50, 60) points
• Fail (5): [0, 50) points

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Knowledge of the following topics will be highly beneficial:

• Basic mathematical tools (e.g., calculus; linear algebra/matrix algebra)
• Probability and statistics (joint, marginal and conditional probability distributions; [higher-order] moments, e.g., expectation and variance)
• Classical econometrics (linear regression; ordinary least squares [OLS]; maximum likelihood)

Slides for review will be provided but not discussed in class.

michael.pfarrhofer@wu.ac.at