MSc Development Finance

Year of entry: 2023

Course unit details:
Econometric Methods for Development

Course unit fact file
Unit code MGDI60031
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by
Available as a free choice unit? Yes

Aims

The course aims to:

  • Endow students with an understanding of the challenges that present themselves when analysing data.
  • Enable students to interpret and critically evaluate empirical research outputs published in leading applied economics and development economics journals.
  • Provide students with practical skills in testing, modelling, and evaluating theories and economic relationships using different types of data obtained from actual data sets.
  • Equip students with the knowledge and skills necessary to carrying out independent good quality empirical work demanded of an academic researcher or practitioner in development economics.
  • Allow students to implement a battery of techniques required to estimate micro and macro relationships specific to development and development economics, using the econometric software package R.

Syllabus

The course is divided into parts:  

Part I: Pre-sessional (during Induction week)

  • Revisions of basic statistical tools for Development
  • Introduction to R/RStudio

Part II: Main sessions-they are built around the following topics stretching over 10 weeks of teaching:

  • Introduction to Data Analysis for Development
  • Bivariate Regression: Diagnostics and Specification Tests
  • Multivariate Regression: Interpretation and Model Choice
  • Dealing with Non-Stationarity of Variables
  • Panel Data for Development (static and long run relationships, panel VAR)
  • Model Testing and Evaluation

Teaching and learning methods

The course adopts hands-on and blended learning techniques, using a mix of lectures, hands-on workshops, small group tutorials, and pre-recorded teaching material. Emphasis is put on hands-on data analysis, research design, model choice and interpretation of regression outputs. In line with its objectives, the teaching in this course will draw on exercises using real-world secondary data and specialist code-based statistical software.

Theoretical concepts are introduced during weekly one-hour lectures. Mathematical proofs are kept at a minimum and lectures will focus on core theories and concepts instead. Students are encouraged to watch weekly mini videos or, alternatively, read relevant textbook chapters to cover mathematical proofs as background readings to support their understanding of theoretical concepts in preparation for the lecture.

Data analysis and software applications are introduced in weekly one-hour workshops. The workshop sessions are accompanied by an annotated log of the codes required to replicate results. Students are asked to replicate software applications in preparation for the workshops. The workshop sessions focus on joint discussion and interpretation of results, linking to the theoretical material covered during lectures.

Students understanding of and skills in data analysis are further supported through exercises which students are asked to prepare for weekly tutorials. Exercises draw on real-world secondary data and economic applications. Students are allocated into small groups to solve these exercises and are required to bring annotated outputs into the tutorial to swap with other groups for peer review.

Students are required to complete the pre-sessional part of the course, which is assessed online. The mark obtained from the test accounts for 10 per cent of the overall grade of this course. The early assessment element provides an indication of gaps in students’ knowledge which will be addressed in the early weeks of the course.

The weekly exercises, which are formative non-graded assessment elements, are complemented by two additional summative assessment elements. A 1,500-word research report which accounts for 30 per cent of the overall grade and an open book take home exam which accounts for 60 per cent of the overall grade. The research report presents analytical results for a given problem set. The report submissions are accompanied by the submission of relevant code and data files. The take home exam consists of a set of questions and exercises which students must complete within a 48-hour turnaround time.

Knowledge and understanding

  • identify features and characteristics of different type of data and empirically evaluate these features.
  • explain core concepts and techniques in data analysis and econometrics in the context of development.
  • gauge the importance of econometrics results in the design of development policies.

 

Intellectual skills

  • empirically evaluate economic theories and models, related to development, by use of actual data sets.
  • critically evaluate applied work in the fields of development economics and development.
  • understand advantages and limitations of each econometric method and their applications in development economics and development.

Practical skills

  • estimate and interpret different models and identify how they relate to each other.
  • conduct different residual and model diagnostic tests and conclude on the adequacy of model choice against data evidence.
  • identify suitable econometric technique(s) relevant to research questions in the fields of development economics and development.
  • use R econometric software packages and write R-code/R-scripts.

Transferable skills and personal qualities

  • conduct applied independent research using econometric and statistical software.
  • Concisely summarise empirical results and compile research reports.
  • be able to assist in the design of policy by proving quantitative evidence produced by drawing on the tools introduced in this module.

Assessment methods

Method Weight
Written exam 60%
Report 30%
Set exercise 10%

Recommended reading

Primary readings for the course are the following two textbooks:

  • Wooldridge J M (2016) Introductory Econometrics: A Modern Approach, 6th edition (or earlier). Cengage Learning, ISBN: 9781305270107.
  • Heiss, F (2020) Using R for Introductory Econometrics, 2nd edition (or earlier). ISBN: 9798648424364. Freely available here: http://www.urfie.net/index.html

The following textbooks are useful additional references:

  • Asteriou D and S G Hall (2011) Applied Econometrics, 2nd edition, Palgrave Macmillan, ISBN:  9780230271821.
  • Banerjee, A, JJ Dolado, JW Galbraith, D Hendry (1993) Co-integration, Error Correction, and the Econometric Analysis of Non-stationary Data, Oxford University Press, ISBN: 9780198288107.
  • Söderbom, M., F. Teal, M. Eberhardt, S. Quinn, A. Zeitlin (2015) Empirical Development Economics, Routledge, ISBN: 9780415810494. https://www.empiricalde.com/  

All students are asked to complete R Studio Primers – The Basics in preparation for this course:

Additional readings on specialised topics and empirical case studies are made available each week via the online learning platform.

Study hours

Scheduled activity hours
Lectures 10
Practical classes & workshops 10
Tutorials 40
Independent study hours
Independent study 90

Teaching staff

Staff member Role
Sophie Van Huellen Unit coordinator

Additional notes

Available as free choice:

  • MSc Development Economics and Policy
  • MSc Development Finance
  • MSc Public Policy and Management
  •  

Available as free choice with appropriate background (students need to obtain approval from the course lead within the first week of the semester):

  • MSc International development Pathways
  • All other University of Manchester PGTs

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