The course focuses on econometric methods for analyzing panel data (longitudinal data) models. We will discuss important methods and illustrate these methods using real life data sets within Stata, and the popular open source software, R, enhancing student’s analytical and professional development within data-driven investigation.
Students do not necessarily need to know R as a prerequisite – an introduction to using R for the analysis of panel data will be demonstrated and explained to you throughout the course. Students are free to choose any software to use while working on their assignment, including Stata, R, Python, and Matlab.
Being equipped with tools and knowledge to analyse panel data sets will advance many student’s professional careers tremendously, as it is a must-have knowledge in many fields of applied research. This will help students excel in the financial sector, in central banking, and in any field that is concerned with data analysis.
A panel data set is a data set where there are observations of many units over multiple time points. For instance, the data set of the GDPs of EU countries over the last century is a panel data set. Using panel data sets is becoming more and more popular and those who can work with data are extremely sought-after, as the availability of such data sets are increasing every day within multiple fields of applied economic and financial research.
The course will consist of three main parts. Each part will focus on a certain aspect of panel data analysis. Additionally, each part will contain computer exercise sessions, in which students will learn how to analyse panel data sets by using Stata and R functions, as well as commands that belong to current methods and applicable techniques.
- The first part of the course will focus on micro panels. Micro panels are panels where there are observations of many individual units, but only over a short period of time. For instance, household surveys are usually of this sort. They require specialized techniques to be analyzed. This part focuses on these specialized techniques and their applications.
- The second part of the course will focus on the diagnostic tests that analyze panel data sets. Panel data exhibits many features that may influence the ways required to estimate the models. We will discuss various tests that have been recently developed in the literature. This part will provide students with an in-depth knowledge of the theory of diagnostics of panel data sets and its applications.
- The third part of the course will discuss the methods used to analyze large panel data sets. Large panels consist of many individual units and cover long time periods. These kind of panels require advanced time series techniques and other innovative techniques to analyze cross-sectional dimension. This part will mainly focus on methods that deal with cross-sectional dependence in various set-ups. Cross-sectional dependence occurs, for example, when the cross-section units are affected by the same unobserved shocks and is present in many panel data sets. There is a vast amount of literature on how to deal with the problems caused by cross-sectional dependence, and we will focus on these methods. This course will discuss how these methods can be applied practically, with illustrations, and with using real data.