Further Statistics for Economics and Econometrics

  • Summer schools
  • Department of Statistics
  • Application code SS-ME117
  • Starting 2019
  • Short course: Open
  • Location: London

The course provides a precise and accurate treatment of probability, distribution theory and statistical inference.

As such there will be a strong emphasis on mathematical statistics as important discrete and continuous probability distributions are covered (such as the Binomial, Poisson, Uniform, Exponential and Normal distributions). Properties of these distributions will be investigated including use of the moment generating function.

Point estimation techniques are discussed including method of moments, maximum likelihood and least squares estimation. Statistical hypothesis testing and confidence interval construction follow, along with non-parametric and goodness-of-fit tests and contingency tables. A treatment of linear regression models, featuring the interpretation of computer-generated regression output and implications for prediction, rounds off the course.

Collectively, these topics provide a solid training in statistical analysis. As such, this course would be of value to those intending to pursue further study in statistics, econometrics and/or empirical economics. Indeed, the quantitative skills developed by the course are readily applicable to all fields involving real data analysis.

Session: Two
Dates: 8 July – 26 July 2019
Lecturer: Dr James Abdey


Programme details

Key facts

Level: 200 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

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


No previous knowledge of statistics will be assumed, although familiarity with elementary statistics to the level of ME116 would be an advantage (for example, descriptive statistics – sample mean and variance). Mathematics to A-level standard or equivalent is highly desirable, i.e. competency with basic calculus, integration and algebraic manipulation (although a refresher document will be provided).

Programme structure

  • Probability
  • Probability distributions               
  • Sampling theory                              
  • Point estimation                              
  • Interval estimation                         
  • Hypothesis testing                         
  • Linear regression                            
  • Goodness-of-fit tests
  • Nonparametric tests

Course outcomes

  • To provide a solid understanding of distribution theory which can be drawn upon when developing appropriate statistical tests.  Useful properties of some important distributions will be reviewed as well as parameter estimation techniques for various probability distributions
  • To facilitate a comprehensive understanding of the main branches of statistical inference, and to develop the ability to formulate the hypothesis of interest, derive the necessary tools to test this hypothesis and interpret the results
  • To introduce the fundamental concepts of statistical modelling, with an emphasis on linear regression models with multiple explanatory variables


Department of Statistics at LSE has a distinguished history. Its roots can be traced back to the appointment of Sir Arthur Lyon Bowley, an alumnus of the University of Cambridge, at LSE in 1895. He was appointed Chair in Statistics in 1919, probably the first appointment of its kind in Britain. The Department of Statistics was submitted jointly to REF 2014 with LSE's Department of Mathematics: 84% of the research outputs of the two departments were classed as either world-leading or internationally excellent in terms of originality, significance and rigour.

The department has an international reputation for development of statistical methodology that has grown from its long history of active contributions to research and teaching in statistics for the social sciences.

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


Reading materials

As stand-alone resources will be provided, there will be no need to rely on a particular text.  There are several good texts at the right level for this course which can be used in support of the course materials, including:

Freedman, D., Pisani, R. and R. Purves (2007) Statistics, Norton, 4th edition.

Larsen, R.J. and M.J. Marx (2011) An Introduction to Mathematical Statistics and Its Applications, Pearson Education, 5th edition.

*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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