# 2023/2024

## Content, progress and pedagogy of the module

The module aims at teaching methods and techniques needed to utilise time series data and panel data. The module is organized as an interaction between theoretical understanding and practical application. The module builds on the obtained knowledge and skills from previous modules in math, statistics, and econometrics I, including the use of relevant software. The module covers basic univariate time series models and multivariate models using time series and panel data. The module covers various forecasting methods as well as multivariate models, aimed at exploring dynamic relationships and causal effects. The module builds the research foundation for the students, which enables them to do an empirical project as well as conduct research during their bachelor thesis.

Upon completion of this module students are expected to be able to deal with real world time-series and panel data. They are expected to be able to find relevant data, identify important patterns in the data, and then perform various econometric applications. They are expected to be able to formulate an appropriate empirical model, estimate its parameters, test hypothesis, and evaluate the model. In addition, students completing this module will be able to read, understand and critically review an empirical report which uses time series and panel data.

### Learning objectives

#### Knowledge

The objective is that the student after the module possesses the necessary knowledge on:

• the properties of time series data and panel data.
• the mechanics of various time series econometric models (ARIMA, ARDL, VARs) and panel data models (Difference-in-differences, Regression Discontinuity, IV regressions).
• how to valuate models, interpret the results, and draw conclusions.

#### Skills

The objective is that the student after the module possesses the necessary skills in:

• applying time series and panel data techniques to construct a statistical model and estimate its parameters using real world data (while using statistical software).
• performing thorough diagnostics of the models and being able to come up with solutions to improve model validity.
• describing, communicating, and interpreting results of the model.

#### Competences

The objective is that the student after the module possesses the necessary competences in:

• identifying real world problems in the field of economics, and addressing those problems empirically by applying time series and panel data techniques.
• relating the results of the model to economic theory and drawing conclusions in the light of empirical evidence.
• evaluating the applied approach (theoretically and empirically), and understanding the limitations and shortcoming of time series and panel models when drawing conclusions.

### Type of instruction

For further information see §17.

## Exam

### Exams

 Name of exam Econometrics II Type of exam Oral exam based on a project ECTS 10 Assessment 7-point grading scale Type of grading Internal examination Criteria of assessment The criteria of assessment are stated in the Examination Policies and Procedures