Introduction to Econometrics with R

About

Quantitative analysis of high dimensional data sets is increasingly used for problem solving in economics, business, and finance. However, a skillful analysis requires profound knowledge of the underlying statistical methods and statistical programming skills.

The objective of this course is to introduce you to fundamental concepts of econometrics and data analysis that form the basis for data driven decision making, empirical analysis of causal relationships, and forecasting. In particular, the concepts that you will learn in this course will equip you with skills and knowledge necessary to excel in more advanced econometrics and applied statistics courses at CBS (e.g., BA-BMECV1031U Econometrics, KAN-COECO1058U Econometrics, KAN-COECO1056U Financial Econometrics, KAN-CMECV1249U Panel Econometrics) and elsewhere. Finally, this course will sharpen your technical skills for problem solving at workplace and in other real-life settings.

Throughout the course, we will learn about matrices and their use in linear regression analysis, probability distributions and their role in carrying out valid data approximations, and estimation methods and their importance in producing credible results of any data analysis.

The course will also introduce you to programming with R, which is the main programming language of statistical computing. We will start out with basic R operations and then, with time, we will learn about ways to write our own functions in R. In this way, you will be set on a path of becoming a statistical programmer

Course leader

Marta Boczon - Department of Economics Ralf Andreas Wilke - Department of Economics

Target group

This is a bachelor level course. CBS Summer University courses at Copenhagen Business School is open to all and welcomes domestic and international students as well as professionals.

Course aim

Analyze and compare different methods of estimation

Carry out basic operations on matrices

Carry out data estimation.

Compute risk and average return on an investment.

Determine the distribution which best suits the data

Determine the most and least likely event

Modify standard R commands.

Read and make use of R help

State properties of distributions and estimation methods.

Fee info