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Econometrics: Methods and Applications
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Coursera 
Overview

Duration

66 hours

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Total fee

Free

Mode of learning

Online

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Credential

Certificate

Econometrics: Methods and Applications
 at 
Coursera 
Highlights

  • Shareable Certificate Earn a Certificate upon completion
  • 100% online Start instantly and learn at your own schedule.
  • Flexible deadlines Reset deadlines in accordance to your schedule.
  • Approx. 66 hours to complete
  • English Subtitles: French, Portuguese (Brazilian), Russian, English, Spanish
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Econometrics: Methods and Applications
 at 
Coursera 
Course details

More 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. 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
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Econometrics: Methods and Applications
 at 
Coursera 
Curriculum

Welcome Module

Welcome to our MOOC on Econometrics

About this course

Course Guide - Structure of the MOOC

Course Guide - Further information

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

Econometrics: Methods and Applications
 at 
Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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    Students Ratings & Reviews

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    Gursimrat Kaur
    Econometrics: Methods and Applications
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    5
    Learning Experience: The course was very useful. The contents were structurally planned, all the lectures were very informative, assignment questions were also available at the end of each week plus training questions were also there fir pratice with provided solutions
    Faculty: The faculty was very experienced, they have cleared all the concepts and were very calm The contend resources and assignments were updated and comprehensive. They were very interesting to solve and cover all the topic
    Course Support: Not much
    Reviewed on 17 Sep 2022Read More
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