Courses Master Display 2016-2017
|Course Description||To PDF|
|Course title||High-Dimensional Econometric Methods for Big Data|
For more information: firstname.lastname@example.org
|Language of instruction||English|
The objective of this course is to provide students with an understanding of modern and advanced econometric techniques for the analysis of high-dimensional data. Students will be able to read and understand theoretical papers on the subject, to implement the techniques themselves in statistical software, and to apply the techniques to data used in economics and business. In addition to gaining this knowledge they will develop the skills to assess such methods critically and consequently adapt them to suit their needs.
In this course we cover several advanced techniques that have recently been developed in econometrics and statistics for the analysis of high-dimensional problems, which often arise in the context of Big Data. We will discuss theoretical properties of the methods, their practical implementation using the statistical programming language R and the application of these methods to real-life economic and financial datasets.
Topics that are covered include:
• Estimation, inference and forecasting in common factor models
• Linear regression with many regressors: model selection (information criteria, cross-validation) and penalized regression (lasso and variants)
• Inference in high-dimensional regression models: post-model selection inference, model averaging, multiple hypothesis testing, construction of ‘honest’ confidence intervals
• Introduction to machine learning techniques for use in econometrics, with applications to high-dimensional discrete choice models
The course will consist of lectures, in which the methods and theory are introduced, and tutorials, in which groups of students present specific papers on the subject. Students also have to write a paper for which they implement and apply the methods to economic problems.
• Hastie, T., R. Tibshirani and J. Friedman (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Ed). Freely available at http://statweb.stanford.edu/~tibs/ElemStatLearn/
• Selected papers and book chapters (to be announced on Eleum)
Students need to have solid background in probability theory, mathematical statistics, econometric methods and time series analysis, comparable to the knowledge obtained during the econometric courses of the bachelor programme Econometrics and Operations Research. In addition, students are advised to have followed (or follow in parallel) the course Time Series Analysis and Dynamic Econometrics.
|Teaching methods||PBL / Presentation / Lecture / Groupwork|
|Assessment methods||Final Paper / Participation / Written Exam|
|Evaluation in previous academic year||For the complete evaluation of this course please click "here"|
|This course belongs to the following programmes / specialisations||