The course develops a comprehensive set of tools and techniques for analyzing time series in economics and finance. The methods will be applied to forecasting problems and other empirical questions by using available datasets. The course teaches how to use a statistical software (mainly R) to apply these methods. The following topics are covered:

1. Exploring and Visualizing Time Series Data: Techniques for organizing, visualizing, and interpreting time series data.

2. Univariate Time Series Models: Methods including ARIMA models for analyzing and forecasting single time series.

3. Multivariate Time Series Models: Advanced methods such as autoregressive distributed lag models, vector autoregressive models, and models suited for large datasets (dynamic factor and machine learning models).

4. Point and Density Forecasting: Methods for generating and interpreting point forecasts and density forecasts.

5. Forecast Evaluation: Techniques for assessing the accuracy and reliability of forecasts.

6. Real-World Application Projects: Conducting complete forecasting projects from data preparation to model implementation and validation, demonstrating practical application of course concepts.

To attend the course / exercise, registration via e-learning platform OLAT is required. Registration is possible from 2 – 27 September 2024. The students themselves are responsible for checking the creditability of the course to their course of study.

Direct link to OLAT course: https://lms.uzh.ch/url/RepositoryEntry/17577443405