BAEcon Development Studies

Scholarships and bursaries, including the Manchester Bursary , are available to eligible home/EU students.

Some undergraduate UK students will receive bursaries of up to £2,000 per year, in addition to the government package of maintenance grants.

You can get information and advice on student finance to help you manage your money.

Students taking this course unit will develop understanding and skills in applying macroeconometric models. They will be able to apply time-series modelling and forecasting techniques and will be able to think in classical as well as Bayesian statistical frameworks. Students will gain an overview of the relevant literature, will develop the ability to  critically evaluate such research and to communicate research findings effectively.

(ECON10072 or ECON20072) and (ECON20110 or ECON20222) and (ECON20532 or ECON20032)

The course unit aims to:

• Develop students' understanding of various empirical macroeconomic models, estimation techniques, and forecasting methods.
• Provide students with the necessary tools and knowledge to conduct research in macroeconometric topics such as time series analysis, forecasting, Bayesian econometrics, and structural modelling.
• Strengthen students skills in implementing time-series modelling techniques in R.
• Equip students with critical thinking skills, the ability to read relevant literature, and proficiency in communicating research results effectively. 
   

Week 1: Introduction to Macroeconometrics

• Overview of the course and topics to be covered
• The role of econometric methods in macroeconomics
• Review of statistical and econometric concepts

Week 2-3: Time Series Analysis and Forecasting

• Stationary and non-stationary time series models
• ARIMA models and their applications
• Vector autoregressive models and impulse response functions
• Forecast evaluation and model selection

Week 4-5: Bayesian Econometrics

• Introduction to Bayesian inference
• Bayesian regression analysis
• Markov Chain Monte Carlo methods
• Applications to macroeconomic data and models

Week 7-8: Structural Modeling in Macroeconomics

• Dynamic stochastic general equilibrium (DSGE) models
• Estimation and inference in DSGE models
• Applications to monetary and fiscal policy analysis
• Model evaluation and robustness checks

Week 10-11: Empirical Applications in Macroeconometrics

• Case studies and applications of macroeconometric techniques
• Empirical research papers and presentations
• Student research projects and presentations

Week 12: Wrap-up and Review

• Review of key concepts and methods covered in the course
• Final exam review and preparation

• Synchronous activities (such as Lectures or Review and Q&A sessions, and tutorials), and guided self-study. 
• Tutorials will be used to review key points in the lectures, develop technical skills needed to understand the key models and empirical evidence covered in the course, and develop communication skills (oral and written).

Analyze and apply various econometric techniques to real-world macroeconomic data, including:

• Understand the assumptions underlying different macroeconomic models and econometric methods.
• Evaluate and interpret empirical results from macroeconomic models.
• Apply econometric techniques to macroeconomic data, including time series analysis, forecasting, Bayesian econometrics, and structural modeling.
• Identify appropriate statistical models to address specific economic questions.
   

Develop skills to critically read and review macroeconometric research papers.

Communicate macroeconometric research findings effectively, including:

• Identify relevant literature and datasets for conducting macroeconometric research.
• Perform independent research projects using macroeconomic data and econometric methods.
• Ability to implement empirical methods using the R programming language.
   

• Present and communicate research findings effectively through written reports, oral presentations, and visual displays.
• Communication and inter-personal skills required to work as a member of a group.
   

Group work assessment - 20%
Computing Test - 10%
Midterm exam 30 minutes - 30%
Exam (End of term) Equivalent of 1.5h on-campus - 40% 

• Class feedback
• Office hours
• Revision sessions
• Discussion boards

Preliminary Reading List

Textbooks:

1. "Bayesian Econometric Methods," by Gary Koop (2003).
2. "Bayesian Analysis of DSGE Models," by Edward P. Herbst and Frank Schorfheide (2015).
3. "Time Series Analysis," by James D. Hamilton (1994).
4. "Time Series Analysis: Forecasting and Control," by George E. P. Box, Gwilym M. Jenkins, and Gregory C. Reinsel (2015).

Papers:

1. Bai, J., & Ng, S. (2002). "Determining the number of factors in approximate factor models." Econometrica, 70(1), 191-221.
2. Sims, C. A. (1980). "Macroeconomics and reality." Econometrica, 48(1), 1-48.
3. Engle, R. F. (1982). "Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation." Econometrica, 50(4), 987-1008.
4. Stock, J. H., & Watson, M. W. (1999). "Business cycle fluctuations in US macroeconomic time series." In J. B. Taylor & M. Woodford (Eds.), Handbook of macroeconomics (Vol. 1, pp. 3-64). Elsevier.

No single textbook provides the necessary material for this course. The course itself will be taught using a combination of textbook chapters, elementary journal articles, and working papers. A full reading list with the readings for each topic will be made available at the beginning of the course and through the a Library Reading List.

Scheduled activity hours

Staff/student contact (Include lectures, seminars/tutorials/workshops, practicals/laboratory work)
26 (2 hours of lectures – 10 weeks,  6 weeks of tutorials/computer labs)

Private study (Include seminar preparation, presentation preparation and essay preparation)
100

Directed reading
74

Total hours
200

Independent study hours

Private study (Include seminar preparation, presentation preparation and essay preparation)
100

Directed reading
74

Additional notes 

Through their pre-requisites students are expected to have good working knowledge of the R programming language.