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Econometrics: Methods and Applications
  • Level Foundation
  • المدة 66 ساعات hours
  • الطبع بواسطة Erasmus University Rotterdam
  • Offered by

عن

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

الوحدات

Welcome to the fascinating world of Econometrics!

    • 2 Videos
    • 2 Readings
Show info about module content

2 Videos

  • Welcome to our MOOC on Econometrics
  • About this course

2 Readings

  • Course Guide - Structure of the MOOC
  • Course Guide - Further information

Dataset for Lectures on Simple Regression

    • 1 Readings
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1 Readings

  • Dataset Simple Regression

1.1 Simple Regression: Motivation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 1.1 on Simple Regression: Motivation

2 Readings

  • Training Exercise 1.1
  • Solution Training Exercise 1.1

1.2 Simple Regression: Representation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 1.2 on Simple Regression: Representation

2 Readings

  • Training Exercise 1.2
  • Solution Training Exercise 1.2

1.3 Simple Regression: Estimation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 1.3 on Simple Regression: Estimation

2 Readings

  • Training Exercise 1.3
  • Solution Training Exercise 1.3

1.4 Simple Regression: Evaluation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 1.4 on Simple Regression: Evaluation

2 Readings

  • Training Exercise 1.4
  • Solution Training Exercise 1.4

1.5 Simple Regression: Application

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 1.5 on Simple Regression: Application

2 Readings

  • Training Exercise 1.5
  • Solution Training Exercise 1.5

Test Exercise 1

    • 1 PeerReview
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1 Peer Review

  • Test Exercise 1

Dataset for Lectures on Multiple Regression

    • 1 Readings
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1 Readings

  • Dataset Multiple Regression

2.1 Multiple Regression: Motivation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 2.1 on Multiple Regression: Motivation

2 Readings

  • Training Exercise 2.1
  • Solution Training Exercise 2.1

2.2 Multiple Regression: Representation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 2.2 on Multiple Regression: Representation

2 Readings

  • Training Exercise 2.2
  • Solution Training Exercise 2.2

2.3 Multiple Regression: Estimation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 2.3 on Multiple Regression: Estimation

2 Readings

  • Training Exercise 2.3
  • Solution Training Exercise 2.3

2.4 Multiple Regression: Evaluation

    • 2 Videos
    • 4 Readings
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2 Videos

  • Lecture 2.4.1 on Multiple Regression: Evaluation - Statistical Properties
  • Lecture 2.4.2 on Multiple Regression: Evaluation - Statistical Tests

4 Readings

  • Training Exercise 2.4.1
  • Solution Training Exercise 2.4.1
  • Training Exercise 2.4.2
  • Solution Training Exercise 2.4.2

2.5 Multiple Regression: Application

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 2.5 on Multiple Regression: Application

2 Readings

  • Training Exercise 2.5
  • Solution Training Exercise 2.5

Test Exercise 2

    • 1 PeerReview
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1 Peer Review

  • Test Exercise 2

Dataset for Lectures on Model Specification

    • 1 Readings
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1 Readings

  • Dataset Model Specification

3.1 Model Specification: Motivation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 3.1 on Model Specification: Motivation

2 Readings

  • Training Exercise 3.1
  • Solution Training Exercise 3.1

3.2 Model Specification: Specification

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 3.2 on Model Specification: Specification

2 Readings

  • Training Exercise 3.2
  • Solution Training Exercise 3.2

3.3 Model Specification: Transformation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 3.3 on Model Specification: Transformation

2 Readings

  • Training Exercise 3.3
  • Solution Training Exercise 3.3

3.4 Model Specification: Evaluation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 3.4 on Model Specification: Evaluation

2 Readings

  • Training Exercise 3.4
  • Solution Training Exercise 3.4

3.5 Model Specification: Application

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 3.5 on Model Specification: Application

2 Readings

  • Training Exercise 3.5
  • Solution Training Exercise 3.5

Test Exercise 3

    • 1 PeerReview
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1 Peer Review

  • Test Exercise 3

Dataset for Lectures on Endogeneity

    • 1 Readings
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1 Readings

  • Dataset Endogeneity

4.1 Endogeneity: Motivation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 4.1 on Endogeneity: Motivation

2 Readings

  • Training Exercise 4.1
  • Solution Training Exercise 4.1

4.2 Endogeneity: Consequences

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 4.2 on Endogeneity: Consequences

2 Readings

  • Training Exercise 4.2
  • Solution Training Exercise 4.2

4.3 Endogeneity: Estimation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 4.3 on Endogeneity: Estimation

2 Readings

  • Training Exercise 4.3
  • Solution Training Exercise 4.3

4.4 Endogeneity: Testing

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 4.4 on Endogeneity: Testing

2 Readings

  • Training Exercise 4.4
  • Solution Training Exercise 4.4

4.5 Endogeneity: Application

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 4.5 on Endogeneity: Application

2 Readings

  • Training Exercise 4.5
  • Solution Training Exercise 4.5

Test Exercise 4

    • 1 PeerReview
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1 Peer Review

  • Test Exercise 4

Dataset for Lectures on Binary Choice

    • 1 Readings
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1 Readings

  • Dataset Binary Choice

5.1 Binary Choice: Motivation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 5.1 on Binary Choice: Motivation

2 Readings

  • Training Exercise 5.1
  • Solution Training Exercise 5.1

5.2 Binary Choice: Representation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 5.2 on Binary Choice: Representation

2 Readings

  • Training Exercise 5.2
  • Solution Training Exercise 5.2

5.3 Binary Choice: Estimation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 5.3 on Binary Choice: Estimation

2 Readings

  • Training Exercise 5.3
  • Solution Training Exercise 5.3

5.4 Binary Choice: Evaluation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 5.4 on Binary Choice: Evaluation

2 Readings

  • Training Exercise 5.4
  • Solution Training Exercise 5.4

5.5 Binary Choice: Application

    • 1 Videos
    • 3 Readings
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1 Videos

  • Lecture 5.5 on Binary Choice: Application

3 Readings

  • Dataset for Lecture 5.5 on Binary Choice: Application
  • Training Exercise 5.5
  • Solution Training Exercise 5.5

Test Exercise 5

    • 1 PeerReview
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1 Peer Review

  • Test Exercise 5

Dataset for Lectures on Time Series

    • 1 Readings
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1 Readings

  • Dataset Time Series

6.1 Time Series: Motivation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 6.1 on Time Series: Motivation

2 Readings

  • Training Exercise 6.1
  • Solution Training Exercise 6.1

6.2 Time Series: Representation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 6.2 on Time Series: Representation

2 Readings

  • Training Exercise 6.2
  • Solution Training Exercise 6.2

6.3 Time Series: Specification and Estimation

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 6.3 on Time Series: Specification and Estimation

2 Readings

  • Training Exercise 6.3
  • Solution Training Exercise 6.3

6.4 Time Series: Evaluation and Illustration

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 6.4 on Time Series: Evaluation and Illustration

2 Readings

  • Training Exercise 6.4
  • Solution Training Exercise 6.4

6.5 Time Series: Application

    • 1 Videos
    • 2 Readings
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1 Videos

  • Lecture 6.5 on Time Series: Application

2 Readings

  • Training Exercise 6.5
  • Solution Training Exercise 6.5

Test Exercise 6

    • 1 PeerReview
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1 Peer Review

  • Test Exercise 6

Case

    • 1 PeerReview
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1 Peer Review

  • Case Project

Building Blocks: Overview of Topics

    • 1 Readings
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1 Readings

  • Structure

Matrices

    • 3 Videos
    • 6 Readings
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3 Videos

  • Lecture M.1: Introduction to Vectors and Matrices
  • Lecture M.2: Special Matrix Operations
  • Lecture M.3: Vectors and Differentiation

6 Readings

  • 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

Probability

    • 2 Videos
    • 4 Readings
Show info about module content

2 Videos

  • Lecture P.1: Random Variables
  • Lecture P.2: Probability Distributions

4 Readings

  • Training Exercise P.1
  • Solution Training Exercise P.1
  • Training Exercise P.2
  • Solution Training Exercise P.2

Statistics

    • 2 Videos
    • 5 Readings
Show info about module content

2 Videos

  • Lecture S.1: Parameter Estimation
  • Lecture S.2: Statistical Testing

5 Readings

  • 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
Francine Gresnigt
Instructors

Francine Gresnigt

Dennis Fok
Instructors

Dennis Fok

Michel van der Wel
Instructors

Michel van der Wel

Erik Kole
Instructors

Erik Kole

Markus Mueller
Instructors

Markus Mueller

Christiaan Heij
Instructors

Christiaan Heij

Wendun Wang
Instructors

Wendun Wang

Richard Paap
Instructors

Richard Paap

Dick van Dijk
Instructors

Dick van Dijk

Philip Hans Franses
Instructors

Philip Hans Franses

Myrthe van Dieijen
Instructors

Myrthe van Dieijen