Content, progress and pedagogy of the module

The course aims at teaching methods and techniques for the analysis of time series data. It is organized as an interaction between theoretical understanding and practical application. Building on knowledge from previous modules in mathematics, statistics, and econometrics, the course introduces both classical and modern approaches to analyzing dynamic data, including the use of relevant software.

The module covers univariate and multivariate time series models, forecasting methods, and techniques for exploring dynamic relationships. The course builds the research foundation of students, preparing them to work with real-world data in empirical projects and to carry out applied research in their bachelor thesis. Upon completion, students are expected to be able to source relevant time series data, identify patterns, specify and estimate models, test hypotheses, and critically evaluate results. They will also be able to read, understand, and assess empirical work that applies time series methods.

Learning objectives

Knowledge

• Develop a broad understanding of the properties and challenges of time series data.
• Understanding the mechanics of various time series models for description and forecasting, including both traditional econometric and more flexible modern approaches.
•  Ability to evaluate models, interpret results, and assess their relevance in light of economic theory and empirical evidence.

Skills

• Apply time series methods to construct statistical models and estimate their parameters using real-world data and statistical software.
• Carry out diagnostic checks, assess model adequacy, and apply appropriate strategies to improve or adjust models.
• Effectively describe, communicate, and interpret the results of empirical time series analysis to both technical and non-technical audiences.

Competences

• Identify and address real-world problems in economics by applying time series methods in an empirical framework.
• Relate model results to economic theory and provide evidence-based conclusions.
• Critically evaluate the applied approaches (both theoretically and empirically), recognize their limitations, and understand the strengths and shortcomings of different time series methods when drawing conclusions.

Type of instruction

For further information see §17.

Exam

Exams