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EC212: Introduction to Econometrics


Course Content

Everyone agrees that evidence-based policy is likely to be more constructive and effective than that based on dogma or fancy. The problem, for those concerned with social or economic policy, is that we seldom have the luxury of being able to undertake controlled experiments of the type conducted by natural scientists. Instead, we have to draw our inferences from the analysis of non-experimental data, and that is the function of econometrics.

This introductory course is intended to serve two constituencies:

  • Professionals: Each year the course is attended by many professionals who have found that the acquisition of econometric skills would be valuable in their work. Included in this category are PhD students, typically in disciplines other      than economics, who are including a serious empirical component in their      dissertations.
  • Undergraduate students: Many participants are college students from other      universities. Those from the US ought to be able to negotiate credit worth at least one semester since the teaching is at the same standard as that for EC220, the regular-year LSE course taken by economics majors, and the course is distinctly more ambitious in both coverage and depth than the typical one-semester introductory econometrics course in the US.

The course is divided into two parts:

Part 1. The first part of this course introduces the statistical tool known as regression analysis applied to cross-sectional data. It begins with the use and properties of the classical linear regression model and then discusses how various technical problems should be handled.  Initially ordinary least squares is the standard technique, but eventually the focus shifts to instrumental variables estimation.

Part 2. The second part of the course discusses the application of the regression model to time series data. 

Topics covered include:

  • Simple Regression Analysis
  • Properties of Regression Coefficients and Hypothesis Testing
  • Multiple Regression Analysis
  • Transformation of Variables
  • Specification of Regression Variables
  • Heteroscedasticity
  • Stochastic Regressors and Measurement Errors
  • Simultaneous Equations Estimation
  • Modelling Dynamic Processes
  • Autocorrelation
  • Logit and Probit (binary choice models)

Analytical depth
The material gives emphasis to the analysis of the finite sample and asymptotic properties of least squares and instrumental variables estimators, and the accompanying implications for statistical inference, under different assumptions concerning the data generation process. The derivation of asymptotic results is coupled with the use of simulation methods to establish finite-sample properties. The course contains many proofs in simple contexts.

Mathematical content
The course uses college algebra supplemented by the differential calculus at a basic undergraduate level when it is appropriate and useful. It does not use matrix algebra.

Examples of simple applications in economics are used throughout. Participants use Stata to fit educational attainment and wage equation models with cross-sectional data and EViews to fit demand functions with time series data. Technical support is provided.

Intuitive understanding 

In addition to its technical content, the course emphasizes the development of intuitive understanding. The aim is that participants should at all times understand why the material is useful and necessary.

Course Outcomes

The objective of this course is to provide the basic knowledge of econometrics that is essential equipment for any serious economist or social scientist, to a level where the participant would be competent to continue with the study of the subject in a graduate programme. 

While the course is ambitious in terms of its coverage of technical topics, equal importance is attached to the development of an intuitive understanding of the material that will allow these skills to be utilised effectively and creatively, and to give participants the foundation for understanding specialized applications through self-study with confidence when needed.

World-class LSE teaching              

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.

It 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

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



Students taking this course in Session Two should refer to the following text:

Wooldridge, J.M, Introductory Econometrics: A Modern Approach  (International Edition, 2013)

Students taking this course in Session Three should refer to the following text:

C. Dougherty, Introduction to Econometrics, Oxford University Press, (5th edition) 2016.

*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



Session: Two

Dates: 10 - 28 July 2017

Dr Marcia Schafgans
Dr Taisuke Otsu

Session: Three

Dates: 31 July - 18 August 2017

Lecturer: Dr Chris Dougherty

(Due to popularity, this course is repeated and can be taken in either of the sessions outlined above.)

Level: 200 level

Fees: Click here for information

Prerequisites: At least one semester of mathematical statistics with a serious analytical treatment of estimation and inference, and at least one semester of multivariate calculus, both passed at a respectable standard

Lectures: 36 hours 

Classes: 18 hours

Assessment*: Two written examinations

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

How to apply?

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*assessment is optional – see FAQs

**You will need to check with your home institution. Read more about credit transfer here.