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Econometrics: Methods and Applications

Approx. 66 hours to complete

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Course Summary

This course teaches the fundamentals of econometrics through the use of real-world data and the latest statistical software. Students will learn how to analyze economic data and make predictions using regression analysis and other techniques.

Key Learning Points

Learn how to apply econometric methods to real-world economic data
Gain proficiency in statistical software such as R and Stata
Develop skills in regression analysis and time series analysis

Learning Outcomes

Apply econometric methods to real-world economic data
Analyze data using regression analysis and time series analysis
Gain proficiency in statistical software such as R and Stata

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics
Familiarity with a statistical software such as R or Stata

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures

Similar Courses

Applied Econometrics
Econometrics: Methods and Applications

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Joshua Angrist
David Card
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Description

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. 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

Outline

Welcome Module
Welcome to our MOOC on Econometrics
About this course
Course Guide - Structure of the MOOC
Course Guide - Further information

Simple Regression
Lecture 1.1 on Simple Regression: Motivation
Lecture 1.2 on Simple Regression: Representation
Lecture 1.3 on Simple Regression: Estimation
Lecture 1.4 on Simple Regression: Evaluation
Lecture 1.5 on Simple Regression: Application
Dataset Simple Regression
Training Exercise 1.1
Solution Training Exercise 1.1
Training Exercise 1.2
Solution Training Exercise 1.2
Training Exercise 1.3
Solution Training Exercise 1.3
Training Exercise 1.4
Solution Training Exercise 1.4
Training Exercise 1.5
Solution Training Exercise 1.5

Multiple Regression
Lecture 2.1 on Multiple Regression: Motivation
Lecture 2.2 on Multiple Regression: Representation
Lecture 2.3 on Multiple Regression: Estimation
Lecture 2.4.1 on Multiple Regression: Evaluation - Statistical Properties
Lecture 2.4.2 on Multiple Regression: Evaluation - Statistical Tests
Lecture 2.5 on Multiple Regression: Application
Dataset Multiple Regression
Training Exercise 2.1
Solution Training Exercise 2.1
Training Exercise 2.2
Solution Training Exercise 2.2
Training Exercise 2.3
Solution Training Exercise 2.3
Training Exercise 2.4.1
Solution Training Exercise 2.4.1
Training Exercise 2.4.2
Solution Training Exercise 2.4.2
Training Exercise 2.5
Solution Training Exercise 2.5

Model Specification
Lecture 3.1 on Model Specification: Motivation
Lecture 3.2 on Model Specification: Specification
Lecture 3.3 on Model Specification: Transformation
Lecture 3.4 on Model Specification: Evaluation
Lecture 3.5 on Model Specification: Application
Dataset Model Specification
Training Exercise 3.1
Solution Training Exercise 3.1
Training Exercise 3.2
Solution Training Exercise 3.2
Training Exercise 3.3
Solution Training Exercise 3.3
Training Exercise 3.4
Solution Training Exercise 3.4
Training Exercise 3.5
Solution Training Exercise 3.5

Endogeneity
Lecture 4.1 on Endogeneity: Motivation
Lecture 4.2 on Endogeneity: Consequences
Lecture 4.3 on Endogeneity: Estimation
Lecture 4.4 on Endogeneity: Testing
Lecture 4.5 on Endogeneity: Application
Dataset Endogeneity
Training Exercise 4.1
Solution Training Exercise 4.1
Training Exercise 4.2
Solution Training Exercise 4.2
Training Exercise 4.3
Solution Training Exercise 4.3
Training Exercise 4.4
Solution Training Exercise 4.4
Training Exercise 4.5
Solution Training Exercise 4.5

Binary Choice
Lecture 5.1 on Binary Choice: Motivation
Lecture 5.2 on Binary Choice: Representation
Lecture 5.3 on Binary Choice: Estimation
Lecture 5.4 on Binary Choice: Evaluation
Lecture 5.5 on Binary Choice: Application
Dataset Binary Choice
Training Exercise 5.1
Solution Training Exercise 5.1
Training Exercise 5.2
Solution Training Exercise 5.2
Training Exercise 5.3
Solution Training Exercise 5.3
Training Exercise 5.4
Solution Training Exercise 5.4
Dataset for Lecture 5.5 on Binary Choice: Application
Training Exercise 5.5
Solution Training Exercise 5.5

Time Series
Lecture 6.1 on Time Series: Motivation
Lecture 6.2 on Time Series: Representation
Lecture 6.3 on Time Series: Specification and Estimation
Lecture 6.4 on Time Series: Evaluation and Illustration
Lecture 6.5 on Time Series: Application
Dataset Time Series
Training Exercise 6.1
Solution Training Exercise 6.1
Training Exercise 6.2
Solution Training Exercise 6.2
Training Exercise 6.3
Solution Training Exercise 6.3
Training Exercise 6.4
Solution Training Exercise 6.4
Training Exercise 6.5
Solution Training Exercise 6.5

Case Project

OPTIONAL: Building Blocks
Lecture M.1: Introduction to Vectors and Matrices
Lecture M.2: Special Matrix Operations
Lecture M.3: Vectors and Differentiation
Lecture P.1: Random Variables
Lecture P.2: Probability Distributions
Lecture S.1: Parameter Estimation
Lecture S.2: Statistical Testing
Structure
Training Exercise M.1
Solution Training Exercise M.1
Training Exercise M.2
Solution Training Exercise M.2
Training Exercise M.3
Solution Training Exercise M.3
Training Exercise P.1
Solution Training Exercise P.1
Training Exercise P.2
Solution Training Exercise P.2
Dataset for Lecture S.1 on Parameter Estimation
Training Exercise S.1
Solution Training Exercise S.1
Training Exercise S.2
Solution Training Exercise S.2

Summary of User Reviews

Discover the world of econometrics with Erasmus University Rotterdam's online course. Learn how to analyze data, make forecasts and test theories. Students praise the course's comprehensiveness and applicability to real-world scenarios.

Key Aspect Users Liked About This Course

The course's comprehensiveness is highly appreciated by students.

Pros from User Reviews

The course is comprehensive and covers a wide range of topics.
The course is applicable to real-world scenarios.
The instructors provide clear explanations and examples.
The course offers interactive quizzes and assignments.
The course provides a good foundation for further study in econometrics.

Cons from User Reviews

The course may be too technical for beginners.
The course requires a strong foundation in mathematics and statistics.
The course may be too time-consuming for some students.
The course does not offer personalized feedback on assignments.
The course may benefit from more practical exercises.

languageEnglish

start dateAvailable now

durationApprox. 66 hours to complete

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

institutionErasmus University Rotterdam

course taken onCoursera

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