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
Created by: Erasmus University Rotterdam
Taught by: Philip Hans Franses, Prof. Dr.
Econometric Institute, Erasmus School of Economics
Taught by: Christiaan Heij, Dr.
Econometric Institute, Erasmus School of Economics
Taught by: Michel van der Wel, Dr.
Econometric Institute, Erasmus School of Economics
Taught by: Dennis Fok, Prof. Dr.
Econometric Institute, Erasmus School of Economics
Taught by: Richard Paap, Prof. Dr.
Econometric Institute, Erasmus School of Economics
Taught by: Dick van Dijk , Prof. Dr.
Econometric Institute, Erasmus School of Economics
Taught by: Erik Kole, Dr.
Econometric Institute, Erasmus School of Economics
Taught by: Francine Gresnigt, PhD candidate
Econometric Institute, Erasmus School of Economics
Taught by: Myrthe van Dieijen, PhD candidate
Econometric Institute, Erasmus School of Economics
| Commitment | 7 weeks of study, 4-8 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Welcome Module
2 videos, 2 readings
- Video: Welcome to our MOOC on Econometrics
- Video: About this course
- Reading: Course Guide - Structure of the MOOC
- Reading: Course Guide - Further information
Simple Regression
By studying this module and by doing the associated exercises, you will understand the motivation and interpretation of the simple regression model. You will be able to estimate the effect of one variable on another one and to perform statistical analysis, including a test on significance of this effect. You will also be able to use simple regression for making predictions and prediction intervals. You will get trained in evaluating the quality of simple regression models and in checking the conditions required for their application on actual data. The leading example considers the effect of price on sales.
5 videos, 11 readings
- Reading: Dataset Simple Regression
- Video: Lecture 1.1 on Simple Regression: Motivation
- Reading: Training Exercise 1.1
- Reading: Solution Training Exercise 1.1
- Video: Lecture 1.2 on Simple Regression: Representation
- Reading: Training Exercise 1.2
- Reading: Solution Training Exercise 1.2
- Video: Lecture 1.3 on Simple Regression: Estimation
- Reading: Training Exercise 1.3
- Reading: Solution Training Exercise 1.3
- Video: Lecture 1.4 on Simple Regression: Evaluation
- Reading: Training Exercise 1.4
- Reading: Solution Training Exercise 1.4
- Video: Lecture 1.5 on Simple Regression: Application
- Reading: Training Exercise 1.5
- Reading: Solution Training Exercise 1.5
Graded: Test Exercise 1
WEEK 2
Multiple Regression
By studying this module and by doing the associated exercises, you will understand the motivation and interpretation of multiple regression models and the properties of the least squares method. You will be able to construct and analyse regression models in terms of matrices, including their statistical analysis and methods for testing. You will get trained in translating research questions into regression models and in investigating practical economic questions by means of regression. The leading example evaluates gender effects on wage.
6 videos, 13 readings
- Reading: Dataset Multiple Regression
- Video: Lecture 2.1 on Multiple Regression: Motivation
- Reading: Training Exercise 2.1
- Reading: Solution Training Exercise 2.1
- Video: Lecture 2.2 on Multiple Regression: Representation
- Reading: Training Exercise 2.2
- Reading: Solution Training Exercise 2.2
- Video: Lecture 2.3 on Multiple Regression: Estimation
- Reading: Training Exercise 2.3
- Reading: Solution Training Exercise 2.3
- Video: Lecture 2.4.1 on Multiple Regression: Evaluation - Statistical Properties
- Reading: Training Exercise 2.4.1
- Reading: Solution Training Exercise 2.4.1
- Video: Lecture 2.4.2 on Multiple Regression: Evaluation - Statistical Tests
- Reading: Training Exercise 2.4.2
- Reading: Solution Training Exercise 2.4.2
- Video: Lecture 2.5 on Multiple Regression: Application
- Reading: Training Exercise 2.5
- Reading: Solution Training Exercise 2.5
Graded: Test Exercise 2
WEEK 3
Model Specification
By studying this module and by doing the associated exercises, you will understand the motivation and interpretation of alternative model specifications. You will be able to use selection criteria to choose the model variables and the functional form. You will also be able make the right data transformations and to model variability by means of dummy variables. You will get trained in model building and model evaluation by means of specification tests. The leading example considers yearly returns on a stock index.
5 videos, 11 readings
- Reading: Dataset Model Specification
- Video: Lecture 3.1 on Model Specification: Motivation
- Reading: Training Exercise 3.1
- Reading: Solution Training Exercise 3.1
- Video: Lecture 3.2 on Model Specification: Specification
- Reading: Training Exercise 3.2
- Reading: Solution Training Exercise 3.2
- Video: Lecture 3.3 on Model Specification: Transformation
- Reading: Training Exercise 3.3
- Reading: Solution Training Exercise 3.3
- Video: Lecture 3.4 on Model Specification: Evaluation
- Reading: Training Exercise 3.4
- Reading: Solution Training Exercise 3.4
- Video: Lecture 3.5 on Model Specification: Application
- Reading: Training Exercise 3.5
- Reading: Solution Training Exercise 3.5
Graded: Test Exercise 3
WEEK 4
Endogeneity
By studying this module and by doing the associated exercises, you will understand the causes and consequences of endogeneity. You will gain intuition for instrumental variables and their use in the two-stage least squares method. You will be able to evaluate the validity of instruments, both in intuitive terms and by means of statistical tests. You will get trained in performing methods of empirical analysis that are required if some of the variables are endogenous. These methods are illustrated to estimate the effect of preliminary courses on obtained grades.
5 videos, 11 readings
- Reading: Dataset Endogeneity
- Video: Lecture 4.1 on Endogeneity: Motivation
- Reading: Training Exercise 4.1
- Reading: Solution Training Exercise 4.1
- Video: Lecture 4.2 on Endogeneity: Consequences
- Reading: Training Exercise 4.2
- Reading: Solution Training Exercise 4.2
- Video: Lecture 4.3 on Endogeneity: Estimation
- Reading: Training Exercise 4.3
- Reading: Solution Training Exercise 4.3
- Video: Lecture 4.4 on Endogeneity: Testing
- Reading: Training Exercise 4.4
- Reading: Solution Training Exercise 4.4
- Video: Lecture 4.5 on Endogeneity: Application
- Reading: Training Exercise 4.5
- Reading: Solution Training Exercise 4.5
Graded: Test Exercise 4
WEEK 5
Binary Choice
By studying this module and by doing the associated exercises, you will understand the motivation and interpretation of logit models for binary choice data. You will understand the method of maximum likelihood to estimate the parameters of these models and you will be able to perform statistical analyses, including parameter testing. You will get trained in interpreting parameter estimates in terms of marginal effects and odd ratios, and in making forecasts with logit models. The practical use of the logit model is illustrated by response data on a direct mailing.
5 videos, 12 readings
- Reading: Dataset Binary Choice
- Video: Lecture 5.1 on Binary Choice: Motivation
- Reading: Training Exercise 5.1
- Reading: Solution Training Exercise 5.1
- Video: Lecture 5.2 on Binary Choice: Representation
- Reading: Training Exercise 5.2
- Reading: Solution Training Exercise 5.2
- Video: Lecture 5.3 on Binary Choice: Estimation
- Reading: Training Exercise 5.3
- Reading: Solution Training Exercise 5.3
- Video: Lecture 5.4 on Binary Choice: Evaluation
- Reading: Training Exercise 5.4
- Reading: Solution Training Exercise 5.4
- Reading: Dataset for Lecture 5.5 on Binary Choice: Application
- Video: Lecture 5.5 on Binary Choice: Application
- Reading: Training Exercise 5.5
- Reading: Solution Training Exercise 5.5
Graded: Test Exercise 5
WEEK 6
Time Series
By studying this module and by doing the associated exercises, you will understand the motivation and interpretation of time series models. You will be able to choose the structure of the time series model, including trends, and you will be able to perform statistical analyses, including testing on individual and common trends. You will get trained in evaluating the forecast performance of time series models and in investigating practical questions by means of time series. Leading examples are yearly passenger miles of two airline companies and the prediction of industrial production.
5 videos, 11 readings
- Reading: Dataset Time Series
- Video: Lecture 6.1 on Time Series: Motivation
- Reading: Training Exercise 6.1
- Reading: Solution Training Exercise 6.1
- Video: Lecture 6.2 on Time Series: Representation
- Reading: Training Exercise 6.2
- Reading: Solution Training Exercise 6.2
- Video: Lecture 6.3 on Time Series: Specification and Estimation
- Reading: Training Exercise 6.3
- Reading: Solution Training Exercise 6.3
- Video: Lecture 6.4 on Time Series: Evaluation and Illustration
- Reading: Training Exercise 6.4
- Reading: Solution Training Exercise 6.4
- Video: Lecture 6.5 on Time Series: Application
- Reading: Training Exercise 6.5
- Reading: Solution Training Exercise 6.5
Graded: Test Exercise 6
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.
Graded: Case Project
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.
7 videos, 16 readings
- Reading: Structure
- Video: Lecture M.1: Introduction to Vectors and Matrices
- Reading: Training Exercise M.1
- Reading: Solution Training Exercise M.1
- Video: Lecture M.2: Special Matrix Operations
- Reading: Training Exercise M.2
- Reading: Solution Training Exercise M.2
- Video: Lecture M.3: Vectors and Differentiation
- Reading: Training Exercise M.3
- Reading: Solution Training Exercise M.3
- Video: Lecture P.1: Random Variables
- Reading: Training Exercise P.1
- Reading: Solution Training Exercise P.1
- Video: Lecture P.2: Probability Distributions
- Reading: Training Exercise P.2
- Reading: Solution Training Exercise P.2
- Reading: Dataset for Lecture S.1 on Parameter Estimation
- Video: Lecture S.1: Parameter Estimation
- Reading: Training Exercise S.1
- Reading: Solution Training Exercise S.1
- Video: Lecture S.2: Statistical Testing
- Reading: Training Exercise S.2
- Reading: Solution Training Exercise S.2
FAQs
How It Works
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Creators
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.
Ratings and Reviews
This course introduces basic and important statistical methods both in conceptual and practical ways. I wish this course were more longer and covered the topics deeper because I really enjoyed learning the topics.
The first time I took this course, I basically "rage quit". I found it difficult to follow the proofs and the heavy use of linear algebra scared me. After one year I returned to it (with more knowledge of the prerequisites) and loved it. This is an outstanding course that covers some common topics in econometrics in good detail. While the course tries to develop your intuition, there is also some work applied to mathematical proofs. The only minor complaint that I have is that it still lacks some material on how to apply the methods using common programming languages or statistical software. Nevertheless, most of the applied assignments can be done using basic commands (at least in R). If you have a good grasp of the prerequisites, I definitely recommend this course.
Excellent review of all major topics in Econometrics. Great case study applications, lots of work but really fun learning experience.
Coursera provides universal access to the world’s best education, partnering with top universities and organizations to offer courses online.
© 2017 Coursera Inc. All rights reserved.
The most strongly recommended.
All the knowledge involved is very difficult. But this is exactly what I want. I view myself as a smart guy. But believe me, you will spend more time than the suggested time cost on the website. But all the payout is worthy.
If you don't have enough statistic knowledge, it's okay. But I think you should go over the optional week 8 materials very carefully before you study this course. And I think week 8 materials are enough.
Every week has 5 parts, and after every part, there is a small training exercise to solid your understanding. And at the end of every week, there is a peer-reviewed test. And believe me, if you complete all the well-designed training and tests 100%, you will no longer fear for the relevant problem.