In­tro­duc­tion to Eco­no­met­rics with R

About

Quantitative analysis 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, and to sharpen your technical skills for problem solving at workplace and in other real-life settings.

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.

The course will also introduce you to programming with R, the main programming language for statistical computing. We will begin with basic R operations and gradually progress to writing our own functions.

Throughout the semester, we will work with real data on 911 calls to the New York City Police Department. In each class, we will analyze a different aspect of the dataset, allowing us to become familiar with its structure, identify data anomalies, learn how to address them, and use econometric tools to extract important insights, assess credibility, avoid over- or under-selling results, and draw meaningful conclusions. By the end of the course, you will be well on your way to becoming a confident statistical programmer, with the ability to apply the tools you learn to any dataset of your choice.

Learning objectives
Unlock R’s hidden tricks: Use R's help documentation to figure out exactly what functions do, which formulas they rely on, and the methods working under the hood.

Take control of R commands: Change R's default settings, build your own functions, and applying them to real data.

Harness the power of matrices: Carry out matrix operations and use them in R to make your code faster and smarter.

Be the decision-maker: Pick the most suitable estimation method, distribution, or type of analysis for real-world problems.

Bridge theory and practice: Derive maximum likelihood (ML) and method of moments (MM) estimators on paper, then code them in R.

Test estimator quality: Work out their properties (bias, consistency, efficiency) and use R to explore them numerically.

Bring distributions to life: Derive their moments, visualize them in R, simulate random draws, calculate exact probabilities, and use them for statistical testing.

Tell the bigger story: Separate “just describing data” from actually drawing conclusions, knowing when theory lets us generalize and when the mean is just… the mean

Course leader

Marta Boczon - Department of Economics (ECON)

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

Fee info