Applied Econometrics and Big Data

  • Summer schools
  • Department of Economics
  • Application code SS-EC320
  • Starting 2022
  • Short course: Open
  • Location: Houghton Street, London

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.

Session: Two
Dates: 11 July - 29 July 2022
Lecturers: Dr Tatiana Komarova and Dr Marcia Schafgans


Programme details

Key facts

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)

*Assessment is optional

**You will need to check with your home institution

For more information on exams and credit, read Teaching and assessment


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 are not required.

Key Topics

  • Overview of Statistical Reasoning and Introduction to Causal Inference

  • Regression models: RCTs, IV and LATE

  • Regression models: DID and Panel Data

  • Maximum Likelihood Estimation: Introduction to Limited Dependent Variable Models

  • Post-estimations diagnostics for linear regression models. Quantile regression and Quantile treatment effects.

  • Introduction to the Generalized Method of Moments (GMM) & Practical Problems In Applied Analysis

  • Dynamic Panel data models.

  • Regression discontinuity design. Regression kink design.

  • Matching methods.

  • Binary Choice Models. Machine learning classification methods.

  • Introduction to bootstrap.

  • Model selection, information criteria, Ridge and Lasso Regression

  • Principal Component Regression

Programme structure and assessment

This course is delivered as a combination of lectures, class discussions and practical exercises. A key feature of the course is the rigorous practical application of applied micro-econometrics analysis to answer important economic questions in a range of fields, which include labour, development, industrial organisation, and finance.

The course is assessed through two examinations: one mid-session examination (40%), one final examination (50%), and a take-home computer-based exercise (10%). Students will receive formative feedback on two exam type assignments, one before each exam.

*Further details will be provided at the beginning of the course.


Course outcomes

  • 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.

Is this course right for you?

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 in practice.

Your department

The LSE Department of Economics is one of the biggest and best in the world, with expertise across the full spectrum of mainstream economics. A long-standing commitment to remaining at the cutting edge of developments in the field has ensured the lasting impact of its work on the discipline as a whole. Almost every major intellectual development within Economics over the past fifty years has had input from members of the department, which counts ten Nobel Prize winners among its current and former staff and students. Alumni are employed in a wide range of national and international organisations, in government, international institutions, business and finance.

The Department of Economics is a leading research department, consistently ranked in the top 20 economics departments worldwide. This is reflected in the 2014 Research Assessment Exercise which recognised the Department's outstanding contribution to the field. According to the REF 2014 results, 56 per cent of the Department’s research output was graded 4 star (the highest category), indicating that it is 'world-leading'. A further 33 per cent was designated 'internationally excellent' (3 star).

On this three week intensive programme, you will engage with and learn from full-time lecturers from the LSE’s economics faculty.

Your faculty

Dr Marcia Schafgans
Associate Professor of Economics, Department of Economics

Dr Tatiana Komarova
Assistant Professor of Economics, Department of Economics

Reading materials

  • Josh Angrist and Steve Pischke, (2009), Mostly Harmless Econometrics, (Princeton University Press).
  • Marno Verbeek, (2017), A Guide to Modern Econometrics, (Wiley).
  • James Stock and Mark Watson, (2011), Introduction to Econometrics, (MIT Press).
  • Gareth James, Daniela Witte, Trevor Hastie and Robert Tibshirani, (2017): An Introduction to Statistical Learning: With Application in R. (Springer). Available for free online.

*A more detailed reading list will be supplied prior to the start of the programme

**Course content, faculty and dates may be subject to change without prior notice

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