Enjoyable Econometrics
The goal of this MOOC is to show that econometric methods are often needed to answer questions. A question comes first, then data are to be collected, and then...
By Philip Hans Franses on Coursera
About This Course
The goal of this MOOC is to show that econometric methods are often needed to answer questions. A question comes first, then data are to be collected, and then finally the model or method comes in. Depending on the data, however, it can happen that methods need to be adapted. For example, where we first look at two variables, later we may need to look at three or more. Or, when data are missing, what then do we do? And, if the data are counts, like the number of newspaper articles citing someone, then matters may change too. But these modifications always come last, and are considered only when relevant. An important motivation for me to make this MOOC is to emphasize that econometric models and methods can also be applied to more unconventional settings, which are typically settings where the practitioner has to collect his or her own data first. Such collection can be done by carefully combining existing databases, but also by holding surveys or running experiments. A byproduct of having to collect your own data is that this helps to choose amongst the potential methods and techniques that are around. If you are searching for a MOOC on econometrics that treats (mathematical and statistical) methods of econometrics and their applications, you may be interested in the Coursera course “Econometrics: Methods and Applications” that is also from Erasmus University Rotterdam.
Topics Covered
Frequently Asked Questions
How much does Enjoyable Econometrics cost?
Visit the Enjoyable Econometrics course page for current pricing and available discounts.
Who teaches Enjoyable Econometrics?
Enjoyable Econometrics is taught by Philip Hans Franses, Erasmus University Rotterdam.
What skill level is Enjoyable Econometrics for?
This course is designed for all levels learners.
Similar Courses
Minitab Applied Statistics & Hypothesis Testing Mastery
EDUCBA
Evaluate and Optimize Enterprise Log Analytics
EDUCBA
Linear Algebra from Elementary to Advanced
Johns Hopkins University
Data Science Fundamentals with Python and SQL
IBM