ÐÓ°ÉÂÛ̳

This course will provide a solid grounding in recent developments in applied micro-econometrics, including state-of-the art methods of applied econometric analysis. Some of these methods are related to work by recent Nobel Prize in Economics winners J. Angrist, D. Card and G. Imbens.

The course will combine both analytical and computer-based (data) material to enable students to gain practical experience in analysing a wide variety of econometric problems. It will also discuss how modern data science approaches can be used to answer important economic questions. Students will be reading various applied economic papers which apply the techniques being taught. Applications that will be considered include labour, development, industrial organisation and finance.

The topics include analysis of matching methods, identification of average, local average and marginal treatment effects using instrumental variables, regression discontinuity, randomised control experiments, post-estimation diagnostics, cross section and panel data with static and dynamic models, binary choice models and binary classification methods in machine learning, maximum likelihood estimation, ridge regression, lasso regression, and principal component regression.

Lectures are complemented with computing exercises using real data in R or Stata.

This course is ideal for advanced undergraduate students, graduate students, early-career academic researchers, and researchers in the public, private or non-profit sector.

Prerequisites: Students should have completed EC212 Introduction to Econometrics or an equivalent undergraduate course in econometrics and be comfortable with calculus.

Familiarity with linear algebra and statistical software R will be helpful but is not required.

Level: 300 level. Read more information on levels in our FAQs

Fees: Please see Fees and payments

Lectures: 36 hours

Classes: 18 hours

Assessment: Two written examinations and one computer-based exercise

Typical credit: 3-4 credits (US) 7.5 ECTS points (EU)

Please note: Assessment is optional but may be required for credit by your home institution. Your home institution will be able to advise how you can meet their credit requirements. For more information on exams and credit, read Teaching and assessment

This course will suit you if you are interested in applying state-of-the-art methods of applied econometric analysis to answer important causal economic questions. You should consider taking this course if you are interested in pursuing a career in consulting or are trying to enhance your skills as an economic researcher. You will be developing your skills in applying the statistical software R to put the methods discussed into practice.

• Demonstrate a solid grounding in recent developments in applied micro-econometrics, including state-of-the art methods of applied econometric analysis and their suitability to answer important economic questions.
• Demonstrate facility with implementing the techniques covered in the course using statistical software on real-world datasets.
• Demonstrate ability to answer economic questions of interest by using applied econometrics techniques.

The design of this course is guided by ÐÓ°ÉÂÛ̳ faculty, as well as industry experts, who will share their experience and in-depth knowledge with you throughout the course.