Econometrics: Methods and Applications
Erasmus University Rotterdam
About this Course
Welcome!
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.
* 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.
* 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
Skills you will gain
EconometricsLinear RegressionData AnalysisEviews
Syllabus - What you will learn from this course
1
Section
Welcome Module
2 videos (Total 9 min), 2 readings
2 readings
Course Guide - Structure of the MOOC11m
Course Guide - Further information10m
Simple Regression
5 videos (Total 39 min), 11 readings, 1 quiz
5 videos
Lecture 1.2 on Simple Regression: Representation7m
Lecture 1.3 on Simple Regression: Estimation7m
Lecture 1.4 on Simple Regression: Evaluation8m
Lecture 1.5 on Simple Regression: Application6m
11 readings
Dataset Simple Regression1m
Training Exercise 1.10m
Solution Training Exercise 1.110m
Training Exercise 1.20m
Solution Training Exercise 1.210m
Training Exercise 1.30m
Solution Training Exercise 1.310m
Training Exercise 1.40m
Solution Training Exercise 1.410m
Training Exercise 1.50m
Solution Training Exercise 1.510m
2
Section
Multiple Regression
6 videos (Total 45 min), 13 readings, 1 quiz
6 videos
Lecture 2.2 on Multiple Regression: Representation9m
Lecture 2.3 on Multiple Regression: Estimation8m
Lecture 2.4.1 on Multiple Regression: Evaluation - Statistical Properties8m
Lecture 2.4.2 on Multiple Regression: Evaluation - Statistical Tests5m
Lecture 2.5 on Multiple Regression: Application9m
13 readings
Dataset Multiple Regression1m
Training Exercise 2.130m
Solution Training Exercise 2.110m
Training Exercise 2.20m
Solution Training Exercise 2.210m
Training Exercise 2.30m
Solution Training Exercise 2.310m
Training Exercise 2.4.10m
Solution Training Exercise 2.4.110m
Training Exercise 2.4.230m
Solution Training Exercise 2.4.210m
Training Exercise 2.50m
Solution Training Exercise 2.510m
3
Section
Model Specification
5 videos (Total 41 min), 11 readings, 1 quiz
5 videos
Lecture 3.2 on Model Specification: Specification9m
Lecture 3.3 on Model Specification: Transformation8m
Lecture 3.4 on Model Specification: Evaluation8m
Lecture 3.5 on Model Specification: Application9m
11 readings
Dataset Model Specification1m
Training Exercise 3.10m
Solution Training Exercise 3.110m
Training Exercise 3.20m
Solution Training Exercise 3.210m
Training Exercise 3.30m
Solution Training Exercise 3.310m
Training Exercise 3.40m
Solution Training Exercise 3.410m
Training Exercise 3.50m
Solution Training Exercise 3.510m
4
Section
Endogeneity
5 videos (Total 44 min), 11 readings, 1 quiz
5 videos
Lecture 4.2 on Endogeneity: Consequences8m
Lecture 4.3 on Endogeneity: Estimation8m
Lecture 4.4 on Endogeneity: Testing7m
Lecture 4.5 on Endogeneity: Application9m
11 readings
Dataset Endogeneity1m
Training Exercise 4.10m
Solution Training Exercise 4.110m
Training Exercise 4.20m
Solution Training Exercise 4.210m
Training Exercise 4.30m
Solution Training Exercise 4.310m
Training Exercise 4.40m
Solution Training Exercise 4.410m
Training Exercise 4.50m
Solution Training Exercise 4.510m
5
Section
Binary Choice
5 videos (Total 45 min), 12 readings, 1 quiz
5 videos
Lecture 5.2 on Binary Choice: Representation9m
Lecture 5.3 on Binary Choice: Estimation9m
Lecture 5.4 on Binary Choice: Evaluation8m
Lecture 5.5 on Binary Choice: Application9m
12 readings
Dataset Binary Choice1m
Training Exercise 5.10m
Solution Training Exercise 5.110m
Training Exercise 5.20m
Solution Training Exercise 5.210m
Training Exercise 5.30m
Solution Training Exercise 5.310m
Training Exercise 5.40m
Solution Training Exercise 5.410m
Dataset for Lecture 5.5 on Binary Choice: Application1m
Training Exercise 5.50m
Solution Training Exercise 5.520m
6
Section
Time Series
5 videos (Total 52 min), 11 readings, 1 quiz
5 videos
Lecture 6.2 on Time Series: Representation10m
Lecture 6.3 on Time Series: Specification and Estimation11m
Lecture 6.4 on Time Series: Evaluation and Illustration10m
Lecture 6.5 on Time Series: Application11m
11 readings
Dataset Time Series1m
Training Exercise 6.10m
Solution Training Exercise 6.110m
Training Exercise 6.20m
Solution Training Exercise 6.210m
Training Exercise 6.30m
Solution Training Exercise 6.310m
Training Exercise 6.40m
Solution Training Exercise 6.410m
Training Exercise 6.50m
Solution Training Exercise 6.510m
7
Section
Case Project
1 quiz
8
Section
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.
7 videos (Total 84 min), 16 readings
7 videos
Lecture M.2: Special Matrix Operations14m
Lecture M.3: Vectors and Differentiation11m
Lecture P.1: Random Variables11m
Lecture P.2: Probability Distributions9m
Lecture S.1: Parameter Estimation11m
Lecture S.2: Statistical Testing13m
16 readings
Structure20m
Training Exercise M.10m
Solution Training Exercise M.110m
Training Exercise M.20m
Solution Training Exercise M.210m
Training Exercise M.30m
Solution Training Exercise M.310m
Training Exercise P.10m
Solution Training Exercise P.110m
Training Exercise P.20m
Solution Training Exercise P.210m
Dataset for Lecture S.1 on Parameter Estimation30m
Training Exercise S.10m
Solution Training Exercise S.110m
Training Exercise S.20m
Solution Training Exercise S.210m
4.5
33%
83%
25%
Top Reviews
By JJ•Nov 16th 2015
The design of the course is very Helpful and efficient. The course is well explained. The instructors are very clear and master the subject. They very detailed and well organized.
By TM•Jun 9th 2016
Very practical, I would urge people who intend to take this course to come to this course with at least some knowledge of econometrics and statistics. It would come in handy.
Instructors
Philip Hans Franses
Prof. Dr.
Dennis Fok
Prof. Dr.
Richard Paap
Prof. Dr.
Dick van Dijk
Prof. Dr.
Erik Kole
Dr.
Francine Gresnigt
PhD candidate
Myrthe van Dieijen
PhD candidateAbout Erasmus University Rotterdam
Erasmus University: a top-100 ranked international research university based in Rotterdam, the Netherlands. Our academic teaching and research focuses on four areas: health, wealth, culture and governance.
Erasmus University Rotterdam: make it happen.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I pay for this course?
If you pay for this course, you will have access to all of the features and content you need to earn a Course Certificate. If you complete the course successfully, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. Note that the Course Certificate does not represent official academic credit from the partner institution offering the course.
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