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# EC113: Further Statistics for Economics and Econometrics

Course Content

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.

Topics covered include:

• 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.

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.

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Texts*

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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KEY FACTS

Session: Two

Dates: 10 - 28 July 2017

Lecturer: Dr James Abdey

Level: 100 level

Prerequisites: No previous knowledge of statistics will be assumed, although familiarity with elementary statistics to the level of EC112 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).

Lectures: 36 hours

Classes: 18 hours

Assessment*: Two written examinations

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

*assessment is optional – see FAQs