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The course is aimed at advanced students of economics, especially master students who are interested in basic methods and current developments in modern macroeconometrics. The course is also suitable for PhD students.

We cover modern theoretical macroeconomics (the study of aggregated variables such as economic growth, unemployment and inflation by means of structural macroeconomic models) and combine it with econometric methods (the application of formal statistical methods in empirical economics). Macroeconometrics is highly computational; therefore, we focus on the practical computational implementation using MATLAB.

The course comprises of three blocks. The first topic provides a basic knowledge of multivariate time series analysis, the second topic deals with structural vector autoregressive models (SVAR) and the third topic with dynamic stochastic general equilibrium models (DSGE). Using these three blocks, the theoretical and methodological foundations of a modern macroeconomist are taught. The students are thus enabled to understand the analyses and forecasts of public (universities, central banks, economic research institutes) as well as private (business banks, political consultations) research departments, but also to derive and empirically evaluate their own structural macroeconomic models.

The course is interactive and "hands-on", so there is no formal separation between the lecture and the exercise class. Each topic begins with a theoretical input and presentation of methods. These concepts are practiced directly thereafter (within the class) by means of exercises and implemented on the computer in MATLAB and DYNARE.

Basic knowledge of macroeconomics as well as econometrics are required, programming skills in Matlab (or R) are advantageous, but not necessary. Please bring a portable computer with installed MATLAB (or Octave) to each class. If you do not have a notebook or have problems with the installation, please contact us so that we can provide you with a device for the class.

To obtain credits for the course, students are required to actively participate in the class as well as hand in three exercise sheets (for each topic) within a period of one week.

Topic 1: Multivariate time series analysis

• Decomposition in trends and cycles: stationarity and cointegration
• Impulse responses, shock decomposition, forecasting

Topic 2: Structural vectorautoregressive models

• Maximum likelihood and Bayesian estimation
• Model checking and fit
• Impulse responses, shock decomposition, forecasting
• Structural restrictions: zero restrictions, sign restrictions, distributional restrictions

Topic 3: Dynamic stochastic general equilibrium models

• New Keynesian models
• Solution methods: log-linearization and perturbation
• Maximum likelihood and Bayesian estimation
• Model checking and fit
• Impulse responses, shock decomposition and forecasting