subject

Coursera: Econometrics: Methods and Applications

 with  Dick van Dijk , Myrthe van Dieijen, Christiaan Heij, Michel van der Wel, Philip Hans Franses, Richard Paap, Dennis Fok, Erik Kole and Francine Gresnigt
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

Syllabus

Welcome Module


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.

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.

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.

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.

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.

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.

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.

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.

5 Student
reviews
Cost Free Online Course (Audit)
Pace Upcoming
Subject Data Analysis
Provider Coursera
Language English
Certificates Paid Certificate Available
Calendar 8 weeks long
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FAQ View All
What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

5 reviews for Coursera's Econometrics: Methods and Applications

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0 out of 1 people found the following review useful
a year ago
Cedric Humbert is taking this course right now.
By studying this course I was hoping to learn about models and test them instead we are doing lousy math "as shown on the slide" and proving basic linear algebra properties... Not really what I was aiming for. The structure is good though as you can expect on Coursera, I will stay for one more week or so to see if it gets better (only going through week 2 right now) .
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1 out of 1 people found the following review useful
2 years ago
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Zoltan Sánchez completed this course and found the course difficulty to be very hard.
The course is very good, but in my opinion too focused on theoretical demostrations above practical examples. Suitable for advanced practitioners.
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0 out of 2 people found the following review useful
2 years ago
Alejandro Rafael Viluce is taking this course right now.
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1 out of 1 people found the following review useful
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0 out of 1 people found the following review useful
2 years ago
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John Walsh partially completed this course.
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