Econometrics: Methods and Applications

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

• Taught by:  Philip Hans Franses, Prof. Dr.

  Econometric Institute, Erasmus School of Economics

• Taught by:  Christiaan Heij, Dr.

  Econometric Institute, Erasmus School of Economics

• Taught by:  Michel van der Wel, Dr.

  Econometric Institute, Erasmus School of Economics

• Taught by:  Dennis Fok, Prof. Dr.

  Econometric Institute, Erasmus School of Economics

• Taught by:  Richard Paap, Prof. Dr.

  Econometric Institute, Erasmus School of Economics

• Taught by:  Dick van Dijk , Prof. Dr.

  Econometric Institute, Erasmus School of Economics

• Taught by:  Erik Kole, Dr.

  Econometric Institute, Erasmus School of Economics

• Taught by:  Francine Gresnigt, PhD candidate

  Econometric Institute, Erasmus School of Economics

• Taught by:  Myrthe van Dieijen, PhD candidate

  Econometric Institute, Erasmus School of Economics

Commitment 7 weeks of study, 4-8 hours/week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.5 stars Average User Rating 4.5See what learners said Coursework

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Syllabus


WEEK 1


Welcome Module



2 videos, 2 readings expand

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



By studying this module and by doing the associated exercises, you will understand the motivation and interpretation of the simple regression model. You will be able to estimate the effect of one variable on another one and to perform statistical analysis, including a test on significance of this effect. You will also be able to use simple regression for making predictions and prediction intervals. You will get trained in evaluating the quality of simple regression models and in checking the conditions required for their application on actual data. The leading example considers the effect of price on sales.


5 videos, 11 readings expand

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



By studying this module and by doing the associated exercises, you will understand the motivation and interpretation of multiple regression models and the properties of the least squares method. You will be able to construct and analyse regression models in terms of matrices, including their statistical analysis and methods for testing. You will get trained in translating research questions into regression models and in investigating practical economic questions by means of regression. The leading example evaluates gender effects on wage.


6 videos, 13 readings expand

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



By studying this module and by doing the associated exercises, you will understand the motivation and interpretation of alternative model specifications. You will be able to use selection criteria to choose the model variables and the functional form. You will also be able make the right data transformations and to model variability by means of dummy variables. You will get trained in model building and model evaluation by means of specification tests. The leading example considers yearly returns on a stock index.


5 videos, 11 readings expand

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



By studying this module and by doing the associated exercises, you will understand the causes and consequences of endogeneity. You will gain intuition for instrumental variables and their use in the two-stage least squares method. You will be able to evaluate the validity of instruments, both in intuitive terms and by means of statistical tests. You will get trained in performing methods of empirical analysis that are required if some of the variables are endogenous. These methods are illustrated to estimate the effect of preliminary courses on obtained grades.


5 videos, 11 readings expand

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



By studying this module and by doing the associated exercises, you will understand the motivation and interpretation of logit models for binary choice data. You will understand the method of maximum likelihood to estimate the parameters of these models and you will be able to perform statistical analyses, including parameter testing. You will get trained in interpreting parameter estimates in terms of marginal effects and odd ratios, and in making forecasts with logit models. The practical use of the logit model is illustrated by response data on a direct mailing.


5 videos, 12 readings expand

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



By studying this module and by doing the associated exercises, you will understand the motivation and interpretation of time series models. You will be able to choose the structure of the time series model, including trends, and you will be able to perform statistical analyses, including testing on individual and common trends. You will get trained in evaluating the forecast performance of time series models and in investigating practical questions by means of time series. Leading examples are yearly passenger miles of two airline companies and the prediction of industrial production.


5 videos, 11 readings expand

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



This Case Project is the final assignment of our MOOC. It is of an applied nature, and it asks you to answer practical questions by means of econometric methods. By doing the case, you will integrate various econometric methods and skills that were trained in our MOOC.

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 expand

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