Objectives

Students who have successfully completed this course have a good grasp of basic techniques of empirical economics, including linear regression and instrumental variables estimation. They are able to apply these techniques using R, in applied economic research leading to a term paper or a BSc thesis. They are well prepared to study more advanced topics in applied econometrics, such as the analysis of time series data and panel data and latent variable models.

Contents

This course provides an introduction to econometric methods, with a strong emphasis on the application of these methods in applied economic research. In the first half of the course, students first acquire a basic understanding of the nature of empirical research and the possibilities and limitations of econometric methods. Then, they study the basics of one of econometrics' key tools, linear regression analysis of cross-sectional data. Along the way, students use the software package R and econometric methods are illustrated with empirical examples and empirical exercises. In the second half of the course, more advanced topics, including regression analysis with heteroskedastic and autocorrelated data, causal inference, instrumental variables estimation, and simultaneous equations models are studied. In addition, students are guided in writing a small research paper in empirical economics.

Specifics

The course (6 ECTS) is taught in 13 weeks, with one 1h45m *lecture* and one 1h45m *workshop session* each week (except week 7; see below). The workshop sessions teach students how to use the software package R for empirical work. The workshop sessions teach students how to use the software package Stata for empirical work, deepen and expand the core material with empirical and other practical exercises and examples, and guide students in preparing their final research paper.

__Weeks 1-6: Introduction and basics of regression analysis__

The first *six lectures* cover:

- the nature of econometrics and economic data (Chapter 1);
- the simple regression model (Chapter 2);
- multiple regression analysis: estimation (Chapter 3);
- multiple regression analysis: inference (Chapter 4);
- a brief discussion of properties of the OLS estimator in large samples (optional reading: Chapter 5).

The *workshop sessions* in weeks 1,3, and 5 are computer lab sessions in which students work with R under guidance of the TA (optional reading: 'Using R for Introductory Econometrics' by Florian Heiss). The remaining sessions are spent on regular exercises, examples, and questions on material covered in the lectures. A selection of topics from Chapters 6 and 7 (optional reading) is covered along the way.

__Week 7: Preparation for the empirical assignment__

After wrapping up any remaining material from weeks 1-6, the seventh *lecture* slot will be used by the TA to hand out, discuss, and prepare for the research paper assignment (optional reading: Chapter 19).

There will be no *workshop sessions* in week 7 (note however that the *workshop sessions* for weeks 1-6 are scheduled at the start of weeks 2-7 this year)

__Weeks 8-13: Some advanced topics__

The last six *lectures* cover:

- heteroskedasticity (Chapter 8);
- aspects of time series analysis and autocorrelated errors (parts of Chapters 10 and 12);
- instrumental variables, simultaneous equations, and causal analysis (Chapters 15 and 16).

The *workshop sessions* in weeks 8,10, and 12 are further computer lab sessions. The remaining sessions are similar to those in the first half of the course, with an additional focus on the research paper assignment.

__Office hours:__ Students with questions and comments that cannot be covered during the lectures and workshop sessions should first contact the TA, by email or in person at times agreed in the tutorials.

__Grading:__ Students are expected to actively participate in all lectures and tutorials. The final grade is based on homework assignments (25%) and a final written exam (75%).

The homework assignments include:

- eleven small problem sets (R and other assignments) that need to completed and handed in individually; and
- an empirical research paper of about 10 pages that should be written and handed in jointly with another student.

The eleven weekly assignments are graded "pass" if there is evidence that students made a serious effort completing the assignment, and "fail" otherwise. Further details on the assignments, including deadlines, are given in the Tutorial and homework instructions under 'Assignments' on Blackboard.
The grade for the homework assignments equals the grade for the research paper times the share of weekly assignment passed. For example, a student who passed all 11 weekly assignments and received a grade 8 for her research paper will receive a grade 8 x 11/11 = 8 for her homework assignments (which in turn contributes 25% to her final grade).

The final exam is an open book exam. Students can bring (a copy of) Woolridge's book, any documents distributed during the course (slides, handhouts, earlier exams, etcetera), their course notes (including annotations in the book, notes on the slides, etcetera), and a dictionary to the exam. They can also bring (any) calculator, but communication devices (including internet-enabled calculators) are __not allowed__.

__Resit policy:__ The final exam can be retaken once this academic year (for 75% of the grade). The homework assignments cannot be retaken during the year; the initial grade also contributes to the final grade for the resit (25%). Students who did not pass the course last (or in an earlier) year need to retake the entire course, including the research paper assignment, this year. Further instructions for students from earlier years are given in the Tutorial and homework instructions under 'Assignments' on Blackboard.

Compulsory Reading

- Jeffrey Wooldridge,
*Introduction to Econometrics: A Modern Approach*, 6th edition, Cengage Learning, 2016. Students can also use recent earlier editions of this book, such as the 2014 EMEA edition or the 2013 International 5th edition, but are themselves responsible for sorting out any differences.
- For the use of R, please check: http://www.urfie.net

Required Prerequisites

Mathematics (30L103 & 30L108), Statistics (30L104) & Microeconomics (30L101 & 30L106).

Compulsory for

- BSc Double Degree Economics (
2013,
2014,
2015,
2016,
2017 )
- BSc EBE, track Economics and Society (
2016,
2017 )
- BSc Economics (
2012,
2013,
2014,
2015,
2016,
2017 )

Recommended option for