SCHOOL OF SLAVONIC AND EAST EUROPEAN STUDIES
ADVANCED QUANTITATIVE METHODS
Course pre-requisute:
Credit value: Open to:
SEES0095 Spring Term 2022/23
SEES0083 Quantitative Methods
15
MA students only
Compulsory for students on the following programmes:
MA Comparative Business Economics
MA Comparative Economics and Policy
MRes in the Politics and Economics of Eastern Europe
MRes in East European Studies (Year 2 Social Sciences track) IMESS (Economics & Business track)
Contents
1. Contact details and office hours 2. Course objectives and profile 3. Resources
4. Organisation of the course
5. The range of AQM 6. A note on software
1. Contact details and office hours
Course leader:
Office hours:
Tutorials:
Office hours:
Dr. Svetlana Makarova ( [email protected] ) see Moodle or SSEES webpage
Dr. Erkin Sagiev ([email protected] ) tbc
2. Course objectives and profile
Pre-requisite: MA SEES0083 Quantitative Methods course (delivered in Term 1).
Course Objectives: This course builds on the earlier core quantitative methods course. It aims at consolidating of the skills and techniques learnt there with the use of new software (Stata) and at developing more advanced statistical and econometric techniques suitable for analysing a range of data across the social sciences and beyond. The course is oriented towards the use of statistical methods and focused principally on the interpretation and validation of results. By the end of the course students will be equipped with the technical, statistical and interpretative skills and will be able to design and carry out independent empirical research projects. These will constitute the foundation skills appropriate for embarking on a social science/economics research degree.
Course profile: This course caters for students from diverse backgrounds with different levels of statistical knowledge. For those with a limited statistical background there is the opportunity to gain more quantitative skills, for those with limited experience in using statistical software classes in Stata will be on help and for those with more experience there is the opportunity to hone skills and undertake original quantitative research at a more advanced level. There is an emphasis throughout on the use, interpretation and understanding of results rather than proofs, which will be kept at an elementary/intermediate level. The course can though act as a lead into a more theoretical econometrically oriented course.
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3. Resources:
There is no single core text for this course as we cater for a range of levels. Many of the key resources are available online. However, there are a couple of books (numbers 1 and 2 in the list below) which refer to Stata directly and others with wider coverage of statistics for Social Scientists (5 and 7); (4) is devoted to time series analysis and (3) is also highly recommended as an introductory text, for anyone serious about using STATA for econometrics.
1. Wooldridge, J. M. (2013), Introductory Econometrics: A Modern Approach. 5th ed., South Western.
2. Stock, J.H. and Watson, M.W (2012), Introduction to Econometrics. 3rd ed., Pearson International Edition.
3. Dougherty, C. (2016) Introduction to Econometrics, 5th ed., OUP, Oxford.
4. Charemza, W. and Deadman, D. (1997), New Directions in Econometric Practice.
2nd ed., Edward Elgar.
5. Verbeek M. A. (2012), Guide to Modern Econometrics, 4th ed., Wiley.
6. Rabe-Hesketh S., Skronda A. (2010) Multilevel and Longitudinal Modeling Using
Stata, 2nd ed., Stata Press.
7. Baum, C. F. (2006) An Introduction to Modern Econometrics Using Stata. Stata
Press.
8. Kohler, U. and Kreuter, F. (2005) Data Analysis Using Stata. Stata Press.
9. Hamilton, Lawrence C. (2003) Statistics with Stata. Duxbury.
There is a range of other accessible texts covering basic applied statistics and some introductory econometrics and the Stata supporting website (http://www.ats.ucla.edu/stat/stata/) is excellent:
4. Organisation of the course:
The lecture notes, class exercises, video recording teaching events, useful links and other materials are all available from the course homepage on Moodle.
The course will be taught during the spring term. The core teaching will consist of lectures describing the methods and their applications. They will be accompanied by workshops and computer classes where students will acquire knowledge of empirical economic modelling. The course materials will all be hosted within the Moodle learning environment (https://moodle.ucl.ac.uk/). To access this, you will need your UCL username and password. The web resources include online lectures, downloadable data sets, class exercises, useful links and a notice board.
The detailed lecture materials are provided largely as a background to facilitate data analysis but different students will use the background materials to enhance their intuitive understanding in different ways.
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Time and Location:
There are 20 hours of lectures: Thursday 11.00- 13.00.
There are also five workshops (three 1-hour and two 2-hour) and five 1.5 hours lab sessions (see Timetable or/and the Academic Calendar and Teaching Activity section on module Moodle webpage).
Summative Assessment: The course will be assessed through 100% coursework assignment. The assignment will be in the form of a project. For your project are you encouraged to use data related to emerging markets and transition economies – for example, on Demographic and Health indicators, voting patterns and determinants, regional GDP, Foreign Direct Investment, Growth, inflation, labour markets etc – and carry out an appropriate statistical analysis. Your results should be written up as a research project incorporating an introduction, a contextual or literature background, a methodology, data description and interpretation, results and conclusions. The practical exercises throughout the course will prepare you for this assignment. You will be expected to use Stata as the only computational tool. No other statistical packages will be allowed. The assignment should be no more than 5,000 words. There will be progress check on the first Monday after the reading week. The initial project proposal deadline is during the week after the reading week.
The assignment should be produced in the style of an academic journal publication and submitted electronically via Turnitin link provided on Moodle course page by 15.00, Monday, 8 May 2023. The data and the do-file that you used to generate your results must also be submitted through Moodle by 15.00, Monday, 8 May 2023, in Stata format and in the form of a data file saved as ‘student_number.dta’ – where you obviously replace ‘student_number’ with your individual student number.
Formative Assessment: in order to help students to follow the theoretical and practical materials the weekly home exercises will be given. Answers and solutions to these exercises will be partially available on Moodle and partially will be discuss during the follow-on sessions and, if needed, during office hours. MCQ test for self-evaluation will be given after lecture 3. Empirical modelling of Yield Curve for Eurobonds, will be given during the reading week. This will allow students not only in following the lectures and classes topics but also in self-evaluation.
5. The range of AQM
Though there is necessarily some use of formulae and mathematical terminology this is not a math course – it is an applied data analysis course. It is not an economics course either but since economics has generated the most sophisticated approaches to empirical analysis, we make use of econometrics texts. ‘Econometrics is the science and art of using statistical techniques to analyse data’. Where there are variables that can be quantified
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there is a role for econometrics even if and when it cannot play the most important role in understanding and/or explaining a particular phenomenon.
There are a variety of issues that we can use applied data analysis to investigate. Examples abound: a) what patterns can we observe in election results? b) do Eastern European students consume more alcohol than their Western counterparts? c) are wages higher in country X than country Y? d) how are we to understand different economic growth performances across countries? e) do privately owned firms perform ‘better’ than publicly owned firms? f) are men happier than women?
6. A note on software
We use an econometrics package called Stata for this course. Stata, version 17, is available either in the UCL remote desktop or for downloading (recommended) from UCL software database page. Instruction for downloading is available on the module Moodle page under section Textbooks; companion websites; Stata..
Full information and supporting materials are available from the Stata web site:
http://www.ats.ucla.edu/stat/stata/
7. Topics (subject adjustments and time permit)
PART I: Multiple regression analysis of cross section data
Lecture 1: Introduction and recap.
Lectures 2 - 4: Classical Linear Regression Model. H heteroscedasticity. Dummy
variables. Checking for Model adequacy. Example: modelling economic growth.
Part II. Econometric modelling of time series data
Lectures 4 – 6: Introduction to modelling of time-series data. Stationarity. Autocorrelation. AR(p) process. Nonstationarity. Unit root and cointegration analysis.
Part III. Modelling with Panel Data
Lecture 7 – 8: Models with Fixed Effects and Random Effects. Introduction to dynamic panel data modelling.
Part IV
Lecture 9: Addressing endogeneity: Generalised Method of Moments (GMM). Lecture 10: Revision & Assessment.
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