Methods Summer Programme 18-29 August 2014 Intensive courses in research methods for students, academics and professionals.
Courses offered in 2014
Factor Models in Time Series with Applications in Macroeconomics and Finance (18-22 August 2014)
Welcome from Professor George Gaskell 2
A graduate-level course about “big data” analysis, introducing methods and techniques for extracting meaningful and useful information from large panels of time series.
Programme Information 3 Ethnographic Methods and Practice 4 Factor Models in Time Series with Applications in Macroeconomics and Finance 6 Intermediate Econometrics 8 Multiple Correspondence Analysis for the Social Sciences 10
Intermediate Econometrics (18-29 August 2014) The typical introductory econometrics course is mostly confined to the Classical Linear Regression Model. This is a second course in econometrics: econometrics beyond the Classical Linear Regression Model.
Qualitative Research Methods 12 Real Analysis 14 Statistical Methods for Social Research using SPSS 16 Survey Methods
The Millennium Cohort Study: Analysing young children’s development from birth to age 11 20 Tools for Macroeconomists: The Essentials 22 Tools for Macroeconomists: Advanced Tools 24
Multiple Correspondence Analysis for the Social Sciences (18-22 August 2014) This course offers a systematic introduction to the principles of multiple correspondence analysis, a method which allows the observation of patterning of complex data sets, as well as practical instruction using SPAD software so that students will be able to use MCA in their own work.
Tools for Macroeconomists: The Essentials (18-22 August 2014) A hands-on graduate-level course teaching key techniques to solve, analyse, and estimate macroeconomic models.
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Ethnographic Methods and Practice (25-29 August 2014) In this intensive one-week course, students will learn how to design and conduct ethnographic fieldwork, integrate ethnography into mixedmethods designs and analyse ethnographic data. The focus will be on the application of this set of methods to understanding real world issues in context, and connections between traditional forms of ethnographic enquiry and emergent visual, digital, and material methods will be emphasised.
Qualitative Research Methods (18-29 August 2014) Qualitative research methods are widely used to provide rich and detailed understandings of peopleâ€™s experiences, interactions, societal discourses or institutional practices. This is an introductory course in qualitative research methods, preparing students to design, carry out, report, read and evaluate qualitative research projects.
Real Analysis (11-29 August 2014) A considerable part of economic theory is difficult to follow without a strong background in real analysis. This course will introduce students to concepts of modern analysis such as continuity, metric spaces, compactness, convexity and integration and will show the connections to economic theory. Note: the first week of this course (11-15 August) will be delivered online.
Statistical Methods for Social Research using SPSS (18-29 August 2014) Data-driven research requires knowledge of the appropriateness of different statistical techniques and the means to perform empirical calculations. This course equips researchers with these tools using the popular SPSS package.
Survey Methods (18-29 August 2014) The social survey is a core methodology in the social sciences, allowing researchers to track social values, behaviour, attitudes, and norms between groups and over time. This course covers all aspects of survey research methods, covering modes of interview, questionnaire design, sampling methods, and analysis of survey results.
Tools for Macroeconomists: Advanced Tools (25-29 August 2014) A graduate-level course teaching stateof-the-art techniques to solve and analyse advanced macroeconomic models.
The Millennium Cohort Study: Analysing young childrenâ€™s development from birth to age 11 (25-29 August 2014) The Millennium Cohort Study is an exceptionally rich, representative, multidisciplinary, longitudinal resource for studying child well-being throughout early childhood. This course will introduce participants to the MCS and the skills needed to analyse it.
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WELCOME FROM PROFESSOR GEORGE GASKELL For the majority of social scientists research methods are a means to an end, not an end in themselves. The “ends” are to get a better handle on the “causes of things”- the LSE’s motto handed down from our founding parents. The “means” are a combination of theoretical insights and research methods to explore social phenomena and to test hypotheses on the basis of empirical data. Research methods are the tool box for social scientists; those with only one basic tool will run into problems. To extend the metaphor, if one only has a hammer some household problems can be solved, eg, putting up a picture hook. But others will not, eg, fixing a leaking pipe. The skilled craftsman has a variety of tools at his or her disposal and knows from experience which tool to use for a particular problem. The same applies to social science; selecting the right method, implementing it with care, and recognising its strengths and limitations are all part of the repertoire of required skills. At LSE’s Department of Methodology our aim is to train social scientists with a sophisticated appreciation of both quantitative and qualitative methods and to provide our students with practical experience of social research across a range of methods. Inevitably, in a programme of limited duration we have more modest aspirations in scope but not in quality. Methods Summer Programme courses are designed to introduce participants to a particular method and to develop through a series of lectures and practical exercises a deeper and useful knowledge of that method; to know when it is well indicated, to understand its conceptual underpinnings, and to gain experience of the method in practice and the interpretation of the results. All the courses are taught by academics steeped in experience of social research and with an enthusiasm for the subject. Methods Summer Programme courses are aimed at students, researchers, academics and social researchers in the public and private sectors. They are designed to help not just those who engage in their own primary research, but also those who use and consume research in their day-to-day work, as well as those who commission and manage research projects. From whatever background, we are committed to delivering a rewarding and enriching LSE experience. Professor George Gaskell Programme Director Methods Summer Programme
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PROGRAMME INFORMATION Programme format All courses are full time, and will generally involve three hours of lectures and 1.5 hours of classes per day, though the teaching format may vary from course to course. Classes provide an opportunity for group discussions, a chance to work through problem sets, and training in relevant software packages. Any software required will be available for use on the LSE network. Students will also be expected to work outside of core teaching hours. Assessment Assessment for most courses will be in the form of a two hour written examination on the last day of the course. Some courses may also involve coursework during the programme. Whilst the examination is not compulsory, it is encouraged. Certificates and transcripts On completion of the programme, a certificate and transcript will be provided. Students who complete all graded assessment (including the final exam) will receive an overall final grade which is shown on the certificate and transcript. An attendance certificate will be available to all students, provided 75 per cent of all classes have been attended.
Tuition fees The tuition fees for each course are shown in the individual course descriptions. Students and staff of any academic institution worldwide are eligible for the reduced academic rate. LSE alumni and previous attendees of the LSE Summer School or Methods Summer Programme are eligible for a 15 per cent discount. Please note: this discount does not apply to attendees of Tools for Macroeconomists courses. Accommodation A number of rooms at LSE halls of residence are available for booking by Methods Summer Programme students. All halls are within walking distance of LSE. Prices start from £38.25 per night for single rooms, and £60 per night for twin/double rooms. For more information, please see our website. How to apply Applications may be made by completing the application form available on our website. Applicants will be required to submit copies of their academic transcripts along with the application form. All applicants are expected to be fluent in spoken and written English. For further information on how to apply, please visit lse.ac.uk/methods
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ETHNOGRAPHIC METHODS AND PRACTICE 25-29 August 2014 Tuition fees Standard rate: £1500 Academic rate: £890 Dr Elena Gonzalez-Polledo Dr Alasdair Jones The purpose of this one-week course is to provide intensive training in contemporary ethnographic methods and practice. During the course students will learn how to design and conduct ethnographic fieldwork, integrate ethnography into mixed-methods designs and analyse ethnographic data. The focus will be on the application of this set of methods to understanding real world issues in context, and connections between traditional forms of ethnographic enquiry and emergent visual, digital, and material methods will be emphasised.
This course is aimed at postgraduates, researchers and professionals who are interested in using ethnographic research methods to understand social settings, relationships and practices. Prerequisites There are no formal prerequisites for this course; however applicants must be at postgraduate level or higher. Experience of undertaking social research (in particular qualitative research) and familiarity with ethnographic approach and writings is desirable but not required. Course benefits This course will provide students with: • a n understanding of how to design and carry out a practicable piece of ethnographic research • a n awareness of contemporary developments in the theory and practice of ethnographic studies
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• h ands-on experience of the collection and analysis of ethnographic fieldwork • a n emphasis on practical approaches to making ethnography part of successful theoretical and applied research • s pecialised teaching by researchers experienced with using ethnographic methods in a variety of projects. Course outline This course will cover core components of the ethnographic approach. Starting with an introduction to ethnographic methods, the course will guide students through research design, data gathering and analysis, as well as relevant ethical issues, emphasising connections between traditional forms of ethnographic enquiry and emergent visual, digital, and material methods and epistemologies. Students will therefore learn about a range of contemporary ethnographic approaches, how to design and conduct ethnographic fieldwork and how to analyse ethnographic data. Course topics will be premised on the view that ethnographic methods cannot only provide a deep understanding of the social life of a particular “field,” but also that they can offer a unique perspective into wider societal relations. Through a practice-based approach, and a focus on contemporary developments in ethnographic methods, this course will provide a solid methodological foundation for using ethnographic research to understand real-world issues in context.
Teaching will be delivered by researchactive qualitative researchers who have used a range of ethnographic methods in their research to date and who are proponents of the use of ethnography in academic and applied research settings alike. The course will comprise lectures and seminars, as well as a London-based field trip in the middle of the week. Interaction between the course leaders and participants will be prioritised throughout. Main texts Burawoy, M. (1991) Ethnography Unbound: Power and Resistance in the Modern Metropolis, Berkeley: University of California Press. Law, J. (2004) After Method: Mess in Social Science Research, London: Routledge. Lury, C. and Wakeford, N. (2012) Inventive Methods: the Happening of the Social, London and NY: Routledge. Rogers, R. (2013) Digital Methods, Cambridge MA: MIT. Seale, C., Gobo, G., Gubrium, J.F. and Silverman, D (2012) Qualitative Research Practice, London: Sage. Van Maanen, J. (2011) Tales of the Field, Chicago: The University of Chicago Press. Assessment Final written examination
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FACTOR MODELS IN TIME SERIES WITH APPLICATIONS IN MACROECONOMICS AND FINANCE 18-22 August 2014 Tuition fees Standard rate: £1500 Academic rate: £890 Dr Matteo Barigozzi Large datasets are becoming increasingly available to researchers and practitioners in many disciplines. In particular, during this “big data” revolution the analysis of high–dimensional time series has become one of the most active subjects of modern statistical methodology with applications in various areas of social science including finance, macroeconomics, and econometrics. Although the value of information is unquestionable, the possibility of extracting meaningful and useful information out of this large amount of data is also of great importance. To this end, several new analytical and computational techniques have been developed under the name of factor models. The aim of this course is to provide an introduction to factor models in time series analysis by teaching students the basic analytical methods and their applications to macroeconomics and finance via the use of MATLAB software. These models are widely used in central banks for forecasting key macroeconomic indicators such as GDP and inflation. They are also used to study the
impact of economic policies on economic activity and in validating models of the economy. Financial institutions adopt factor models for risk management. This course is designed for postgraduates, academics and professionals with an interest in big data analysis and some analytical background in time series analysis. Prerequisites At least one semester of mathematical statistics with analytical treatment of estimation and inference, and at least one semester of multivariate calculus. A good background in methods of regression modelling and some basic familiarity with the analysis of multivariate time series. Course benefits After successful completion of the course, participants should be able to: • identify macroeconomic and/or financial policy problems that can benefit from factor analysis and consequently identify the appropriate dataset and methodology to be used • extract and analyse relevant information from large datasets • a pply the analytical tools of time series analysis to the data using MATLAB software • conduct empirical research in time series, ie, to interpret the information extracted
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from the data in a critical way also in relation to the existing literature • forecast time series using many predictors.
• p olicy analysis problems, ie, the study of the dynamic reaction of observed variables to unexpected changes in policies such as monetary policies • optimisation of financial portfolios.
Course outline The course consists of five daily lectures of three hours each, supported by four two hour computer-based practical classes which will allow course participants to implement the lecture material in MATLAB. We will be covering the following topics: • Motivations: the availability of large panels of time series and the value of information in macroeconomics, finance and other disciplines. • Exact and approximate factor models: the curse and blessing of dimensionality. We start by discussing principal component analysis as a useful dimension reduction technique for large panels of time series. This is the most simple example of factor model (the static model) which we then generalise to include all temporal relations among the considered variables (the dynamic model). • Estimation of factor models: we compare different models and discuss their estimation. The basic tools of multivariate time series analysis such as vector autoregressions and the Kalman filter will be introduced. We then apply these models to three main areas: • forecasting in real time of macroeconomics indicators such as Gross Domestic Product
All topics are of particular relevance for and widely used by researchers in central banks and national or international institutions. Examples based on real-data applications and taken from existing papers are presented and discussed during lectures and replicated during computer workshops. Main text There is no main textbook for this course. Lecture notes and all necessary material will be provided. Software used MATLAB. No previous experience is expected. Assessment Final written examination
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INTERMEDIATE ECONOMETRICS 18-29 August 2014 Tuition fees Standard rate: £2310 Academic rate: £1365 Dr Christopher Dougherty The typical introductory econometrics course is mostly confined to the Classical Linear Regression Model. It may also cover a few advanced topics, but with eclectic choice and superficial treatment. This course aims to build a solid, comprehensive understanding of the use of the regression model when one progresses from the CLRM, with its strong and unrealistic assumptions, and addresses the issues that researchers encounter in practice. This course is designed for those who have already taken an introductory course covering the CLRM and need to develop their skills to the next level. The course is not suitable for those whose only exposure to econometrics has been as part of a financial statistics course. 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. The CLRM foundation is a mandatory prerequisite and applicants should be careful to provide evidence of meeting it.
Course benefits Students will have an advanced understanding of the use of the regression model beyond the CLRM. They should then be able to apply econometric techniques to real-world issues. The course also serves as preparation for an MSc-level econometrics course. Course outline The course assumes that participants will already have a sound understanding of the CLRM. After a brief review of the latter, it will cover the following topics: • stochastic regressors • measurement error • instrumental variables • simultaneous equations estimation. Next, it will cover binary choice models: • linear probability model and its shortcomings • logit model • probit model • tobit model • sample selection bias. The course will then treat issues related to regressions with time series data: • regressions with lagged variables • autoregressive distributed lag - ADL (p,q) - models
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• detailed treatment of issues relating to the ADL(1,0) model • consequences of the violation of the assumption of noncontemporaneous independence of regressors and disturbance term • autocorrelation: consequences, tests, and remedies • properties of nonstationary time series • tests for nonstationarity • cointegration • error correction models. The course will conclude with a brief treatment of panel data regressions. The course will not use linear (matrix) algebra.
This course will consist of three hours of lectures each morning and a small-group class lasting one hour and a half each afternoon. A written problem set will be due for each class. Main text Dougherty, C. (2011) Introduction to Econometrics, Oxford: Oxford University Press. Chapters 8 to 14. Software used Stata and EViews will be used for the problem sets. Assessment Final written examination
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MULTIPLE CORRESPONDENCE ANALYSIS FOR THE SOCIAL SCIENCES 18-22 August 2014 Tuition fees Standard rate: £1500 Academic rate: £890 Professor Brigitte Le Roux Professor Johs Hjellbrekke Professor Mike Savage Dr Daniel Laurison This course offers an introduction to MCA, which is a method that allows researchers to observe the patterning of complex data sets through representing categorical variables as points in N-dimensional space. Although it was developed from the late 1960s, MCA has not previously had a large Anglophone following, but it is an increasingly popular method because of (a) its association with Pierre Bourdieu’s high profile sociology, (b) its capacity to lend itself to visualisation of clusters and (c) its potential for mixed methods research. This course is suitable for: • P hD students, postdoctoral fellows and academic staff in the social sciences, who are interested in one of the main methods for the clustering of categorical data • those interested in learning about the methods used by Pierre Bourdieu for the analysis of cultural fields and social relations
• m arket researchers, other commercial researchers, and public sector professionals wishing to learn MCA as a means of clustering complex data sets, and presenting attractive and intuitive visualisations. Prerequisites Applicants must be at PhD level or higher. No statistical knowledge is necessary, but it will be advantageous. Course benefits This course will provide students with: • a comprehensive introduction to MCA • training in how to use SPAD software • a wareness of key exemplars in social science using MCA and an awareness of the theoretical principles it draws upon.
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Course outline This course will offer a comprehensive introduction to the principles of multiple correspondence analysis. Comprehensive training is also provided in using SPAD software, the most accessible and flexible package to use when carrying out MCA. Issues covered include mathematical principles of geometric data analysis, the difference between the active space of modalities and the use of supplementary variables, coding issues, working with the cloud of modalities and the cloud of individuals, clustering methods within MCA, and the use of inferential statistics within MCA. The course is designed to allow the beginner to grasp basic mathematical principles of geometric data analysis. The course will be delivered via a series
of lectures by leading international experts in MCA in the morning, with practical sessions in a computer lab in the afternoons. Main text Brigitte Le Roux and Henry Rouanet (2010) Multiple Correspondence Analysis, QASS nยบ 163. SAGE. Software used SPAD Assessment Practical assignment completed over the duration of the course.
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QUALITATIVE RESEARCH METHODS 18-29 August 2014 Tuition fees Standard rate: £2310 Academic rate: £1365 Dr Jen Tarr Dr Aude Bicquelet The purpose of this course is to equip students to sensitively and critically design, carry out, report, read, and evaluate qualitative research projects. It is taught by a team of qualitative research experts who regularly use the methods which they teach, making the course particularly practical and realistic. It covers the full cycle of a qualitative research project,
from design, to data collection, analysis, reporting and disseminating. The course has the dual aims of equipping students with both conceptual understandings of current academic debates regarding different methods, and the practical skills to put those methods into practice. This course is ideal for postgraduates, academics and professionals with an interest in using qualitative methods to undertake social research. Prerequisites There are no formal prerequisites; however applicants should be at postgraduate level or higher. This course assumes little or no knowledge of qualitative methods.
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Course benefits This course will provide students with: • a solid understanding of the core methods of qualitative data collection and analysis • critical skills in interpreting and evaluating reports of qualitative studies • experience in putting qualitative skills into practice • realistic and practical teaching from established researchers who put these tools to use in their ongoing research projects. Course outline This course presents the fundamentals of qualitative research methods. It covers the classic sources of qualitative data: interviews, focus groups, participant observation and documentary and historical work. It also discusses the challenges and opportunities of new media including visual images and Internet research. Three widely-used data analysis methods are covered: thematic analysis, content analysis and discourse analysis. In addition, issues including quality indicators, research design, ethics, writing up and disseminating are also addressed. The format of the course is a combination of lectures and seminars. Lectures are interactive and introduce the key conceptual issues of each method, as well as giving practical guidance. Lectures also incorporate critical discussion of sample
papers from the peer-reviewed literature, developing students’ skills in critically evaluating reports of qualitative research. Seminars provide hands-on experience of the core methods, including training in the software package NVivo. Main texts Seale, C., Gobo, G., Gubrium, J.F. and Silverman, D. (2004). Qualitative research practice, London: Sage. Bauer, M.W. and Gaskell, G. (2000) Qualitative Researching with Text, Image and Sound, London: Sage. Flick, U. (2009) An Introduction to Qualitative Research, London: Sage. Software used NVivo Assessment Final written examination
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REAL ANALYSIS Online component: 11-15 August 2014 On-campus component: 18-29 August 2014 Tuition fees Standard rate: £2310 Academic rate: £1365 Dr Eleni Katirtzoglou Real analysis is the area of mathematics dealing with real numbers and the analytic properties of real-valued functions and sequences. It studies concepts such as continuity, differentiation and integration and it can be roughly described as rigorous calculus with real numbers. In this course we shall also develop concepts from calculus in more abstract settings such as Euclidean spaces and more general metric spaces. Real analysis is part of the foundation for further study in mathematics as well as graduate studies in economics. A considerable part of economic theory is difficult to follow without a strong background in real analysis. For example the concepts of compactness and convexity play an important role in optimisation theory and thus in microeconomics. This course in real analysis is designed to meet the needs of economics students who are planning to study at postgraduate level. It is also suitable for mathematics and statistics students.
Prerequisites A course on multivariate calculus and linear algebra. Furthermore, students need to be familiar with methods of proofs, basic set theory and the properties of real numbers. A revision of this material will be covered in the Foundations part of the course and can also be found in Introduction to Real Analysis by Robert G. Bartle and Donald R. Sherbert (2011) [Chapters 1 and 2 and Appendix A]. Course benefits After completing this course students will: • gain knowledge of concepts of modern analysis, such as continuity, metric spaces, convexity and integration • develop a higher level of mathematical maturity combined with the ability to think analytically • be able to follow more advanced treatments of real analysis and study its applications in disciplines such as economics. Course outline In 2014 we will be introducing a new course format called Tree Course. The tree course has three parts: Foundations, Core and Branches. Foundations (11-15 August 2014) This part of the course will be delivered online, and will consist of: • video lectures, delivered in short segments • lecture notes, exercises and solutions.
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Topics covered include:
• preliminaries: revision of logic, methods of proof, set theory, and properties of real numbers
Branch 1: Convexity
• sequences in ℝ
• infinite series in ℝ.
On the Friday of this week, students will be asked to complete an online diagnostic quiz. This is not an exam, and passing it is not a prerequisite of attending the course. It is instead intended to give the lecturer an idea of what will need to be looked at on the first afternoon campus session. The completion of the diagnostic test is mandatory. Core (18-22 August 2014) This part of the course will be delivered on campus over five lectures and four classes. Topics covered include: • metric spaces • continuity in ℝ and in metric spaces • differentiation in ℝ and in Euclidean spaces
• fixed-point theorems.
Branches (25-29 August 2014) Students will select one of two branches (Convexity or Integration) to attend in the last week. Each branch will be delivered on campus over four lectures and three classes. Students will be asked to select their branch before arriving, and will have until midway through the first week at LSE to finalise their choice.
• convex functions and sets • separation theorems • applications Branch 2: Integration • Riemann integral • Riemann-Stieltjes integral • applications Main text There is no set text for this course. A comprehensive course pack with course notes and exercises will be supplied. Suggested references Introduction to Real Analysis by Robert G. Bartle and Donald R. Sherbert (2011) Introduction to Metric and Topological Spaces by W. A. Sutherland (1995) Principles of Mathematical Analysis by W. Rudin (1976) The Elements of Real Analysis, by Robert G. Bartle, 2nd edition (1976) Convex Functions by A. Wayne Roberts and Dale E. Varberg. (1973) The following book is a useful, more advanced complementary reading: Real Analysis with Economic Applications by Efe A. Ok (2007). Assessment Final written examination
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STATISTICAL METHODS FOR SOCIAL RESEARCH USING SPSS 18-29 August 2014 Tuition fees Standard rate: £2310 Academic rate: £1365 Dr James Abdey
appropriate for different types of data • inexperience with using statistical software packages (specifically SPSS here) • not knowing how to interpret output from software packages and what conclusions can be drawn.
Many researchers in the social sciences use SPSS to perform data analysis, but often formal training in use of the software and how to interpret output is severely lacking. This course aims to eliminate the quantitative skills deficit which exists among many social science researchers.
Prerequisites A foundation course in statistics at undergraduate level.
This course will concentrate on transforming participants into competent and confident users of SPSS to enable them to conduct independent data analysis for their own research needs. Working with datasets, the course will cover widely-used statistical methods including descriptive statistics, data visualisation, statistical inference, categorical data, correlation and regression, analysis of variance and multivariate analysis (such as factor analysis).
• perform independent data analysis in the social sciences
This applications-oriented course is designed for researchers who lack the confidence to perform data analysis independently due to: • a lack of understanding of various statistical methods • not knowing which techniques are
Course benefits After successful completion of the course, participants should be able to:
• determine which statistical method is appropriate in a given situation and be able to discuss the merits and limitations of a particular method • use SPSS to analyse datasets and be able to interpret output • draw appropriate conclusions following empirical analysis. Course outline This course takes a more applied approach to conventional statistics by focusing on encouraging participants to “get their hands dirty with data”. Instead of being purely theory-oriented, emphasis will be more on the practical application of a variety of statistical techniques to supplied datasets.
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Topics covered in the course will be wideranging, such that participants will be exposed to a variety of statistical methods reflecting the different sorts of data which a researcher may be required to analyse. Assumptions, merits and limitations of methods will be discussed. The course will begin with an overview of the SPSS environment, followed by data visualisation and descriptive statistics. Other topics to be covered include interval estimation and hypothesis testing (for one and two samples), categorical data, correlation and regression, analysis of variance and several multivariate analysis techniques, such as factor analysis. The course will consist of daily lectures supported by computer-based practical classes which will allow course participants
to practise implementing the lecture material hands-on in SPSS. SPSS is a popular choice of statistical software and is ideally suited for empirical research in the social sciences. Main text Field, A. (2009) Discovering Statistics Using SPSS (3rd ed.), Sage. However, a course pack will be provided which will serve as background reading. Software used SPSS (currently version 21) Assessment Final written examination â€ƒ
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SURVEY METHODS 18-29 August 2014 Tuition fees Standard rate: £2310 Academic rate: £1365 Dr Jonathan Jackson Dr Jouni Kuha The social survey is a core methodology in the social sciences. Surveys allow researchers to track social values, behaviour, attitudes, and norms between groups and over time. Graduate students, early career academic researchers, researchers in the public and private sector, and managers/ commissioners of research are increasingly
dealing with surveys, whether as consumers, users or producers of research. This course focuses on data collection, questionnaire design, and data analysis in survey research. This course is ideal for advanced undergraduate students, graduate students, early career academic researchers, and researchers in the public and private sector. Prerequisites Understanding of basic descriptive statistics and inference, eg, chi-square, t-tests, simple linear regression. Course benefits Participants will: • gain a comprehensive understanding of the full survey research process. Whether as a user, consumer or producer of research, participants will be able to assess the quality of existing surveys and be able to conduct their own high quality survey • gain an in-depth understanding of the different ways of conducting survey research, including face-to-face, internet, postal and telephone • learn about the fundamental links between concepts, measures and empirical data in the context of survey research, including specifying the key constructs, developing indicators of key constructs, and following principles of questionnaire design
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â€˘ gain experience in analysing survey data, covering regression models and factor analysis Course outline This course provides an introduction to the methodology of the design and analysis of social surveys. It is intended both for participants who plan to design and collect their own surveys, and for those who need to understand and use data from existing largescale surveys. Lectures and practical seminars cover basic ideas of target populations, survey estimation and inference, sampling error and non-sampling error; sample design and sampling theory; methods of data collection; survey interviewing; cognitive processes in answering survey questions; design and evaluation of survey questions; analysis of survey data using regression modelling and latent variable modelling; and accessing, preparing and working with secondary data from existing social surveys. The course includes computer classes using the statistical computer packages Stata and MPlus. Main texts Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E. and Tourangeau, R. (2009) Survey Methodology. Hoboken, New Jersey: Wiley. Heeringa, S. G., West, B. T. and Berglund, P. A. (2010). Applied Survey Data Analysis. CRC Press. Software used Stata and MPlus Assessment Final written examination
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THE MILLENNIUM COHORT STUDY: ANALYSING YOUNG CHILDREN’S DEVELOPMENT FROM BIRTH TO AGE 11 25-29 August 2014 Tuition fees Standard rate: £1500 Academic rate: £890 Professor Lucinda Platt (LSE), with colleagues from the Centre for Longitudinal Studies, Institute of Education This one-week course will enable students to gain a strong practical understanding of the richness and research potential of the Millennium Cohort Study (MCS), one of the UK’s world-renowned birth cohort studies. The MCS has followed a nationally representative UK-wide sample of children born in 20002001 across their early years, and has been at the forefront of expanding the potential of data collection from children and their families via in-home surveys. The course will provide participants with an understanding of the different data elements, practical issues such as weighting and linking individuals over time and appropriate longitudinal analytical techniques, using all five of the available data sweeps (ages 9 months, 3 years, 5 years, 7 years, and 11 years old). This course is suitable for those with backgrounds across the social and health sciences and for those with an interest in childhoods, survey practice and survey methodology. In particular, the course is aimed at those with an interest in:
• child physical, emotional and cognitive development across the early years, as well as family context and parenting • new approaches to collecting data from children • extending their analytical and data management skills with complex large, nationally representative data sets • utilising multidisciplinary data and approaches to address questions of child well-being and family dynamics • developing their understanding of longitudinal approaches and the benefits of longitudinal data for evaluating causal processes. Prerequisites Some experience of analysis of survey data and experience in using either SPSS or Stata for data management and analysis. This course is particularly suitable for those who have some experience in the production and/or analysis of cross-sectional data and who want to extend their skills and knowledge to survey data. Course benefits On completion of the course, participants will have: • a clear insight into the value and collection of nationally representative, longitudinal survey data and the particular issues in surveying young children
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• a good understanding of the Millennium Cohort Study and its potential for investigating multiple aspects of children’s lives • the ability to conduct data management tasks and conduct analyses across multiple sweeps and multiple respondents to the survey • a grasp of relevant longitudinal techniques that can be applied to cohort studies. Course outline The objective of this course is to introduce participants interested in children, child welfare and cognitive, emotional and physical development, family dynamics and parenting, survey practice and the benefits of longitudinal data to an exceptional multidisciplinary resource, and provide them with the skills to analyse it appropriately. The course will be delivered through a combination of taught sessions and hands-on lab sessions to build up knowledge and enable practical exploration of the data and worked examples, exploiting the multiple sweeps (surveys at five points throughout infancy and childhood up to age 11) and multiple elements of the study (eg, parent interviews, child assessments, child self-completion questionnaires, direct physical measurements). The course will be delivered by experts in the fields of survey design, survey practice, longitudinal analysis and child well-being and development. Coverage will include the following areas: • key features of the Millennium Cohort Study (MCS), eg, sample, national
coverage, different respondents, different types of data, including physical measurements and cognitive assessments as well as questionnaire and self-report data • issues and solutions in the collection of data from a diverse sample of young children • research examples of existing high quality research utilising the MCS – approaches, methods, insights and applications • data management for complex longitudinal data, issues and practical implementation • techniques for analysing children’s development over time and family change using repeat observations • extensions to other child cohorts and cross-national cohort comparisons. Main text No single text book is applicable to this course. Instead, course materials will be specifically developed for the taught and hands-on elements of the course. A reading list covering both the analytical and survey research elements of the course will be supplied. Software used Stata and SPSS will be used for the data management and analysis lab sessions. Assessment Practical assignment completed over the duration of the course.
22 • Methods Summer Programme 18-29 August 2014
TOOLS FOR MACROECONOMISTS: THE ESSENTIALS 18-22 August 2014 Tuition fees Standard rate: £2300 Academic rate: £715 PhD students are eligible for a £150 discount* Professor Wouter den Haan Dr Petr Sedlacek
Prerequisites Basic knowledge of DSGE models (eg, Euler equation, state variables, Bellman equation). Some knowledge of MATLAB. Course benefits This course will provide students with: • a chance to learn a solid set of different tools to analyse and estimate modern macroeconomic models
This is a hands-on graduate-level course teaching key techniques to analyse and estimate macroeconomics models. It teaches the key building blocks of numerical analysis such as function approximation and numerical integration. The course shows how these techniques are used in perturbation and projection methods to accurately solve nonlinear dynamic stochastic models. Relevant theoretical aspects such as the Blanchard-Kahn conditions and the possibility of sun spots solutions are also covered. The course also teaches the tools to estimate such models (Kalman filter, Bayesian estimation, MCMC). Students are taught how to use Dynare, but also how to write MATLAB programs to solve a variety of models with other techniques. In addition to teaching techniques, the course also focuses on practical problems that researchers run into when using these techniques.
• a better understanding of the properties of modern macroeconomic models
This course is aimed at graduate students and academics.
• Impulse response functions
• a better understanding of the importance of nonlinearities • a better understanding of the limitations of popular techniques. Course outline In the morning sessions, a lecture is given by one of the two instructors. In the afternoon sessions, students (typically in groups) work on computer assignments with the help of the instructors and teaching assistants. Monday - Solving and analysing your first DSGE model • State variables • Policy rules (ie, the recursive solution to DSGE models)
• Perturbation analysis
*This course is co-sponsored by the ESRC Centre for Macroeconomics. The support of the Economic and Social Research Council (ESRC) is gratefully acknowledged. .
Methods Summer Programme 18-29 August 2014 • 23
• Certainty equivalence
• State space form
• Maximum Likelihood
• Using the homotophy idea to get good initial values of the steady state (often the hardest part of running Dynare)
• Avoiding the singularity problem
• Parameter values and properties of basic neoclassical model
Friday - Bayesian estimation • Bayesian estimation • MCMC
• Stylised facts
• Metropolis Hastings
Tuesday - Key tools from the numerical approximation literature and projection methods • Numerical integration (Gaussian quadrature)
Software used Dynare and MATLAB
• Function approximation (splines and polynomials) • Projection methods • Endogenous grid points • Fixed-point iteration • Time iteration Wednesday - Topics • Parameterised Expectations Algorithm • Value Function Iteration • Accuracy tests: Euler errors, Dynamic Euler equation test, DHM statistic • Occasionally binding constraints and penalty functions • Blanchard-Kahn conditions • Sun spots and self-fulfilling expectations Thursday - Kalman filter and full information methods • Kalman filter
Assessment No formal assessment
24 • Methods Summer Programme 18-29 August 2014
TOOLS FOR MACROECONOMISTS: ADVANCED TOOLS 25-29 August 2014
models with perturbation methods (Dynare) and projection methods.
Tuition fees Standard rate: £2300 Academic rate: £715 PhD students are eligible for a £150 discount*
Course benefits This course will provide students with:
Professor Wouter den Haan Dr Pontus Rendahl
• a chance to learn the ins and outs of VARs
This graduate-level course teaches stateof-the-art techniques to solve and analyse advanced models. In particular, models with heterogeneous agents, models with boundedly rational agents and/or learning, and also models in which the economy can be at the zero lower bound for the policy interest rate. The course also teaches advanced time-series techniques such as Bayesian VARs with constant and timevarying parameters. In addition to teaching techniques, the course also focuses on practical problems that researchers run into when using these methods.
• a chance to learn a solid set of advanced tools to analyse non-trivial modern macroeconomic models
• a better understanding of models with heterogeneous agents • a better understanding of models with learning and boundedly rational agents.
This course is aimed at graduate students and academics who already have some knowledge of numerical methods such as Dynare and value function iteration.
Course outline The first two days are spent on advanced time-series techniques such as Bayesian VARs with constant and time-varying parameters. The next two days are spent on algorithms to solve models with heterogeneous agents. In addition, a simple algorithm is taught to solve a model in which the policy interest rate is occasionally constrained by the zero lower bound. The last day is spent on models with heterogeneous agents and considers cases when agents do not use rational expectations to make forecasts but instead use Bayesian or least-squares learning. Agent-based models are also considered.
Prerequisites Knowledge of DSGE models (eg, Euler equation, state variables, Bellman equation). Some knowledge of MATLAB. Some knowledge on solving simple DSGE
In the morning sessions, a lecture is given by one of the two instructors. In the afternoon sessions, students (typically in groups) work on computer assignments with the help of the instructors and teaching assistants.
*This course is co-sponsored by the ESRC Centre for Macroeconomics. The support of the Economic and Social Research Council (ESRC) is gratefully acknowledged. .
Methods Summer Programme 18-29 August 2014 • 25
Monday and Tuesday - Advanced empirical techniques • Filtering data • Calculating standard errors for business cycles statistics • Simulated method of moments and GMM • Indirect inference • Spectral analysis • Reduced-form VARs • Structural VARs • Identification (eg, Cholesky decomposition) • Sign restrictions • VARs with time-varying coefficients
heterogeneous agents and aggregate uncertainty • Calculating the ergodic distribution of the Aiyagari model in one step • Solving a model with occasionally binding constraint. In particular, models in which the policy rate can be at the zero lower bound Friday - Learning and boundedly rational agents • Bayesian learning • Least-squares learning • Learning and the parameterized expectations algorithm • Learning and asset pricing (AdamsMarcet) • Expectational stability (E-stability)
Wednesday and Thursday - Solving and simulating models with rational heterogeneous agents and models with occasionally binding constraints • Aggregation • Approximate aggregation
• Multiple equilibria • Learning of exogenous and endogenous variables • Models with rule-of-thumb agents • Agent-based models
• Cross-sectional distributions as state variables
• Finite-horizon learning
• Incomplete markets
• Combining agent-based simulations with forward looking rational agents
• Aiyagari and Huggett model • Solving the Aiyagari model using iterative methods • Krusell and Smith algorithm to solve models with heterogeneous agents and aggregate uncertainty • Xpa algorithm to solve models with
• Solving agent-based models with also some truly rational agents Software used Dynare and MATLAB Assessment No formal assessment
lse.ac.uk/methods Methods Summer Programme The London School of Economics and Political Science Houghton Street London WC2A 2AE Tel: +44 (20) 3199 5380 Email: email@example.com