Econometrics: Methods and Applications (Coursera)

Econometrics: Methods and Applications (Coursera)

Do you wish to know how to analyze and solve business and economic questions with data analysis tools? Then Econometrics by Erasmus University Rotterdam is the right course for you, as you learn how to translate data into models to make forecasts and to support decision making.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

  • What do I learn?

When you know econometrics, you are able to translate data into models to make forecasts and to support decision making in a wide variety of fields, ranging from macroeconomics to finance and marketing. Our course starts with introductory lectures on simple and multiple regression, followed by topics of special interest to deal with model specification, endogenous variables, binary choice data, and time series data. You learn these key topics in econometrics by watching the videos with in-video quizzes and by making post-video training exercises.

  • Do I need prior knowledge?

The course is suitable for (advanced undergraduate) students in economics, finance, business, engineering, and data analysis, as well as for those who work in these fields. The course requires some basics of matrices, probability, and statistics, which are reviewed in the Building Blocks module. If you are searching for a MOOC on econometrics of a more introductory nature that needs less background in mathematics, you may be interested in the Coursera course “Enjoyable Econometrics” that is also from Erasmus University Rotterdam.

  • What literature can I consult to support my studies?

You can follow the MOOC without studying additional sources. Further reading of the discussed topics (including the Building Blocks) is provided in the textbook that we wrote and on which the MOOC is based: Econometric Methods with Applications in Business and Economics, Oxford University Press. The connection between the MOOC modules and the book chapters is shown in the Course Guide – Further Information – How can I continue my studies.

  • Will there be teaching assistants active to guide me through the course?

Staff and PhD students of our Econometric Institute will provide guidance in January and February of each year. In other periods, we provide only elementary guidance. We always advise you to connect with fellow learners of this course to discuss topics and exercises.

  • How will I get a certificate?

To gain the certificate of this course, you are asked to make six Test Exercises (one per module) and a Case Project. Further, you perform peer-reviewing activities of the work of three of your fellow learners of this MOOC. You gain the certificate if you pass all seven assignments.
Have a nice journey into the world of Econometrics!
The Econometrics team

Syllabus

WEEK 1
Simple Regression

WEEK 2
Multiple Regression

WEEK 3
Model Specification

WEEK 4
Endogeneity

WEEK 5
Binary Choice

WEEK 6
Time Series

WEEK 7
Case Project
This Case Project is the final assignment of our MOOC. It is of an applied nature, and it asks you to answer practical questions by means of econometric methods. By doing the case, you will integrate various econometric methods and skills that were trained in our MOOC.

WEEK 8
OPTIONAL: Building Blocks
By studying this module, you get the required background on matrices, probability and statistics. Each topic is illustrated with simple examples, and you get hands-on training by doing the training exercise that concludes each lecture. Three lectures on matrices show you the basic terminology and properties of matrices, including transpose, trace, rank, inverse, and positive definiteness. Two lectures on probability teach you the basics of univariate and multivariate probability distributions, especially the normal and associated distributions, including mean, variance, and covariance. Finally, two lectures on statistics present you with the basic ideas of statistical inference, in particular parameter estimation and testing, including the use of matrix methods and probability methods.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Machine Learning: Concepts and Applications (Coursera) Coursera
University of Chicago

Machine Learning: Concepts and Applications (Coursera)

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.

Sep 21st 2026
5-12 Weeks
Interprofessional Healthcare Informatics (Coursera) Coursera
University of Minnesota

Interprofessional Healthcare Informatics (Coursera)

Interprofessional Healthcare Informatics is a graduate-level, hands-on interactive exploration of real informatics tools and techniques offered by the University of Minnesota and the University of Minnesota's National Center for Interprofessional Practice and Education. We will be incorporating technology-enabled educational innovations to bring the subject matter to life. Over the 10 modules, we will create a vital online learning community and a working healthcare informatics network.

Sep 14th 2026
5-12 Weeks
Fundamentals of Data Analysis in Excel (Coursera) Coursera
Corporate Finance Institute

Fundamentals of Data Analysis in Excel (Coursera)

Excel is the most widely used analysis tool in the world and a great starting point for diving into data analysis. In this course, you’ll apply Excel’s native tools to structure your data into spreadsheets and tables. You’ll then analyze and produce insights from that data using pivot tables. Finally, you’ll visualize those insights by building a dashboard in Excel. You’ll apply these skills using modern functionality like dynamic array formulas, linked data types, and Ideas in Excel. You’ll work hands-on with real-world scenarios, using datasets pulled from financial statements and retail sales.

Sep 21st 2026
5-12 Weeks
Doing Economics: Measuring Climate Change (Coursera) Coursera
University of London,University College London,CORE

Doing Economics: Measuring Climate Change (Coursera)

This course will give you practical experience in working with real-world data, with applications to important policy issues in today’s society. Each week, you will learn specific data handling skills in Excel and use these techniques to analyse climate change data, with appropriate readings to provide background information on the data you are working with. You will also learn about the consequences of climate change and how governments can address this issue.

Sep 21st 2026
4 Weeks
A Scientific Approach to Innovation Management (Coursera) Coursera
Università Bocconi

A Scientific Approach to Innovation Management (Coursera)

How can innovators understand if their idea is worth developing and pursuing? In this course, we lay out a systematic process to make strategic decisions about innovative product or services that will help entrepreneurs, managers and innovators to avoid common pitfalls. We teach students to assess the feasibility of an innovative idea through problem-framing techniques and rigorous data analysis labelled ‘a scientific approach’.

Sep 21st 2026
5-12 Weeks
Assessing Health Program Delivery (Coursera) Coursera
Johns Hopkins University

Assessing Health Program Delivery (Coursera)

This course provides in-depth knowledge about implementation strength, quality of care, and service utilization, which are essential components of health program delivery. This course is primarily aimed at implementers, managers, funders, and evaluators of health programs in low- and middle-income settings (LMISs) targeting women and children, and undergraduate and graduate students in health-related fields.

Sep 21st 2026
5-12 Weeks
Data Science Ethics (Coursera) Coursera
University of Michigan

Data Science Ethics (Coursera)

What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches? This course provides a framework to analyze these concerns as you examine the ethical and privacy implications of collecting and managing big data. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency as you gain a deeper understanding of the importance of a shared set of ethical values.

Sep 21st 2026
4 Weeks
Analysis and Interpretation of Large-Scale Programs (Coursera) Coursera
Johns Hopkins University

Analysis and Interpretation of Large-Scale Programs (Coursera)

This course is for implementers, managers, funders, and evaluators of health programs targeting women and children in low- and middle-income countries as well as undergraduate and graduate students in health-related fields. Course participants will learn how to 1) transform quantitative components of an evaluation measurement plan into a sound analysis plan to address the evaluation questions, 2) conduct quantitative analyses of primary or secondary surveys or other available data, 3) interpret the meaning of the analysis results and their implications, and 4) disseminate the evaluation findings to program implementers, local and global stakeholders.

Sep 21st 2026
5-12 Weeks
Process Mining: Data science in Action (Coursera) Coursera
Eindhoven University of Technology

Process Mining: Data science in Action (Coursera)

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis.

Sep 21st 2026
5-12 Weeks
Mathematical Biostatistics Boot Camp 1 (Coursera) Coursera
Johns Hopkins University

Mathematical Biostatistics Boot Camp 1 (Coursera)

This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.

Sep 14th 2026
4 Weeks