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Econometrics: Methods and Applications

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HomeData ScienceProbability and Statistics

Econometrics: Methods and Applications

Erasmus University Rotterdam

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. * 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


Created by:  Erasmus University Rotterdam
Erasmus University Rotterdam

  • Philip Hans Franses

    Taught by:  Philip Hans Franses, Prof. Dr.

    Econometric Institute, Erasmus School of Economics

  • Christiaan Heij

    Taught by:  Christiaan Heij, Dr.

    Econometric Institute, Erasmus School of Economics

  • Michel van der Wel

    Taught by:  Michel van der Wel, Dr.

    Econometric Institute, Erasmus School of Economics

  • Dennis Fok

    Taught by:  Dennis Fok, Prof. Dr.

    Econometric Institute, Erasmus School of Economics

  • Richard Paap

    Taught by:  Richard Paap, Prof. Dr.

    Econometric Institute, Erasmus School of Economics

  • Dick van Dijk

    Taught by:  Dick van Dijk , Prof. Dr.

    Econometric Institute, Erasmus School of Economics

  • Erik Kole

    Taught by:  Erik Kole, Dr.

    Econometric Institute, Erasmus School of Economics

  • Francine Gresnigt

    Taught by:  Francine Gresnigt, PhD candidate

    Econometric Institute, Erasmus School of Economics

  • Myrthe van Dieijen

    Taught by:  Myrthe van Dieijen, PhD candidate

    Econometric Institute, Erasmus School of Economics
Commitment7 weeks of study, 4-8 hours/week
Language
English
How To PassPass all graded assignments to complete the course.
User Ratings
4.5 stars
Average User Rating 4.5See what learners said
Syllabus
WEEK 1
Welcome Module
2 videos, 2 readings
  1. Video: Welcome to our MOOC on Econometrics
  2. Video: About this course
  3. Reading: Course Guide - Structure of the MOOC
  4. Reading: Course Guide - Further information
Simple Regression
5 videos, 11 readings
  1. Reading: Dataset Simple Regression
  2. Video: Lecture 1.1 on Simple Regression: Motivation
  3. Reading: Training Exercise 1.1
  4. Reading: Solution Training Exercise 1.1
  5. Video: Lecture 1.2 on Simple Regression: Representation
  6. Reading: Training Exercise 1.2
  7. Reading: Solution Training Exercise 1.2
  8. Video: Lecture 1.3 on Simple Regression: Estimation
  9. Reading: Training Exercise 1.3
  10. Reading: Solution Training Exercise 1.3
  11. Video: Lecture 1.4 on Simple Regression: Evaluation
  12. Reading: Training Exercise 1.4
  13. Reading: Solution Training Exercise 1.4
  14. Video: Lecture 1.5 on Simple Regression: Application
  15. Reading: Training Exercise 1.5
  16. Reading: Solution Training Exercise 1.5
Graded: Test Exercise 1
WEEK 2
Multiple Regression
6 videos, 13 readings
  1. Reading: Dataset Multiple Regression
  2. Video: Lecture 2.1 on Multiple Regression: Motivation
  3. Reading: Training Exercise 2.1
  4. Reading: Solution Training Exercise 2.1
  5. Video: Lecture 2.2 on Multiple Regression: Representation
  6. Reading: Training Exercise 2.2
  7. Reading: Solution Training Exercise 2.2
  8. Video: Lecture 2.3 on Multiple Regression: Estimation
  9. Reading: Training Exercise 2.3
  10. Reading: Solution Training Exercise 2.3
  11. Video: Lecture 2.4.1 on Multiple Regression: Evaluation - Statistical Properties
  12. Reading: Training Exercise 2.4.1
  13. Reading: Solution Training Exercise 2.4.1
  14. Video: Lecture 2.4.2 on Multiple Regression: Evaluation - Statistical Tests
  15. Reading: Training Exercise 2.4.2
  16. Reading: Solution Training Exercise 2.4.2
  17. Video: Lecture 2.5 on Multiple Regression: Application
  18. Reading: Training Exercise 2.5
  19. Reading: Solution Training Exercise 2.5
Graded: Test Exercise 2
WEEK 3
Model Specification
5 videos, 11 readings
  1. Reading: Dataset Model Specification
  2. Video: Lecture 3.1 on Model Specification: Motivation
  3. Reading: Training Exercise 3.1
  4. Reading: Solution Training Exercise 3.1
  5. Video: Lecture 3.2 on Model Specification: Specification
  6. Reading: Training Exercise 3.2
  7. Reading: Solution Training Exercise 3.2
  8. Video: Lecture 3.3 on Model Specification: Transformation
  9. Reading: Training Exercise 3.3
  10. Reading: Solution Training Exercise 3.3
  11. Video: Lecture 3.4 on Model Specification: Evaluation
  12. Reading: Training Exercise 3.4
  13. Reading: Solution Training Exercise 3.4
  14. Video: Lecture 3.5 on Model Specification: Application
  15. Reading: Training Exercise 3.5
  16. Reading: Solution Training Exercise 3.5
Graded: Test Exercise 3
WEEK 4
Endogeneity
5 videos, 11 readings
  1. Reading: Dataset Endogeneity
  2. Video: Lecture 4.1 on Endogeneity: Motivation
  3. Reading: Training Exercise 4.1
  4. Reading: Solution Training Exercise 4.1
  5. Video: Lecture 4.2 on Endogeneity: Consequences
  6. Reading: Training Exercise 4.2
  7. Reading: Solution Training Exercise 4.2
  8. Video: Lecture 4.3 on Endogeneity: Estimation
  9. Reading: Training Exercise 4.3
  10. Reading: Solution Training Exercise 4.3
  11. Video: Lecture 4.4 on Endogeneity: Testing
  12. Reading: Training Exercise 4.4
  13. Reading: Solution Training Exercise 4.4
  14. Video: Lecture 4.5 on Endogeneity: Application
  15. Reading: Training Exercise 4.5
  16. Reading: Solution Training Exercise 4.5
Graded: Test Exercise 4
WEEK 5
Binary Choice
5 videos, 12 readings
  1. Reading: Dataset Binary Choice
  2. Video: Lecture 5.1 on Binary Choice: Motivation
  3. Reading: Training Exercise 5.1
  4. Reading: Solution Training Exercise 5.1
  5. Video: Lecture 5.2 on Binary Choice: Representation
  6. Reading: Training Exercise 5.2
  7. Reading: Solution Training Exercise 5.2
  8. Video: Lecture 5.3 on Binary Choice: Estimation
  9. Reading: Training Exercise 5.3
  10. Reading: Solution Training Exercise 5.3
  11. Video: Lecture 5.4 on Binary Choice: Evaluation
  12. Reading: Training Exercise 5.4
  13. Reading: Solution Training Exercise 5.4
  14. Reading: Dataset for Lecture 5.5 on Binary Choice: Application
  15. Video: Lecture 5.5 on Binary Choice: Application
  16. Reading: Training Exercise 5.5
  17. Reading: Solution Training Exercise 5.5
Graded: Test Exercise 5
WEEK 6
Time Series
5 videos, 11 readings
  1. Reading: Dataset Time Series
  2. Video: Lecture 6.1 on Time Series: Motivation
  3. Reading: Training Exercise 6.1
  4. Reading: Solution Training Exercise 6.1
  5. Video: Lecture 6.2 on Time Series: Representation
  6. Reading: Training Exercise 6.2
  7. Reading: Solution Training Exercise 6.2
  8. Video: Lecture 6.3 on Time Series: Specification and Estimation
  9. Reading: Training Exercise 6.3
  10. Reading: Solution Training Exercise 6.3
  11. Video: Lecture 6.4 on Time Series: Evaluation and Illustration
  12. Reading: Training Exercise 6.4
  13. Reading: Solution Training Exercise 6.4
  14. Video: Lecture 6.5 on Time Series: Application
  15. Reading: Training Exercise 6.5
  16. Reading: Solution Training Exercise 6.5
Graded: Test Exercise 6
WEEK 7
Case Project
    Graded: Case Project
    WEEK 8
    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.
    7 videos, 16 readings
    1. Reading: Structure
    2. Video: Lecture M.1: Introduction to Vectors and Matrices
    3. Reading: Training Exercise M.1
    4. Reading: Solution Training Exercise M.1
    5. Video: Lecture M.2: Special Matrix Operations
    6. Reading: Training Exercise M.2
    7. Reading: Solution Training Exercise M.2
    8. Video: Lecture M.3: Vectors and Differentiation
    9. Reading: Training Exercise M.3
    10. Reading: Solution Training Exercise M.3
    11. Video: Lecture P.1: Random Variables
    12. Reading: Training Exercise P.1
    13. Reading: Solution Training Exercise P.1
    14. Video: Lecture P.2: Probability Distributions
    15. Reading: Training Exercise P.2
    16. Reading: Solution Training Exercise P.2
    17. Reading: Dataset for Lecture S.1 on Parameter Estimation
    18. Video: Lecture S.1: Parameter Estimation
    19. Reading: Training Exercise S.1
    20. Reading: Solution Training Exercise S.1
    21. Video: Lecture S.2: Statistical Testing
    22. Reading: Training Exercise S.2
    23. Reading: Solution Training Exercise S.2

    FAQs
    How It Works
    Coursework
    Coursework

    Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

    Help from Your Peers
    Help from Your Peers

    Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

    Certificates
    Certificates

    Earn official recognition for your work, and share your success with friends, colleagues, and employers.

    Creators
    Erasmus University Rotterdam
    Erasmus University: a top-100 ranked international research university based in Rotterdam, the Netherlands. Our academic teaching and research focuses on four areas: health, wealth, culture and governance. Erasmus University Rotterdam: make it happen.
    Ratings and Reviews
    Rated 4.5 out of 5 of 595 ratings
    Daniela Castillo

    EXCELENTE

    Andrey Polishchuchenko

    I'd be happy to have more practical excersises during the course instead/together with formula transformation tasks.

    JG

    Very practical info, well tought

    Gustavo Rocha e Oliveira

    I totally recomend this course for those who are seeking to learn this challenging subject. The material presented in the course provides a great stimulous to stay on track and follow the course until the end.



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