Topics in Applied Macroeconometrics

ECON33022 Topics in Applied Macroeconometrics 20 Semester 2 Undergraduate Level 3 Yizhou Kuang Unit coordinator Economics 1 FHEQ level (Framework for Higher Education Qualifications) ' Last part of a Bachelors ' European Credit Transfer & Accumulation System Rating : 10.0 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. 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. The course unit aims to:<ul> <li>Develop students' understanding of various empirical macroeconomic models, estimation techniques, and forecasting methods.</li> <li>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.</li> <li>Strengthen students skills in implementing time-series modelling techniques in R.</li> <li>Equip students with critical thinking skills, the ability to read relevant literature, and proficiency in communicating research results effectively.   </li></ul> Analyze and apply various econometric techniques to real-world macroeconomic data, including:<ul> <li>Understand the assumptions underlying different macroeconomic models and econometric methods.</li> <li>Evaluate and interpret empirical results from macroeconomic models.</li> <li>Apply econometric techniques to macroeconomic data, including time series analysis, forecasting, Bayesian econometrics, and structural modeling.</li> <li>Identify appropriate statistical models to address specific economic questions.  </li></ul> Develop skills to critically read and review macroeconometric research papers. Communicate macroeconometric research findings effectively, including:<ul> <li>Identify relevant literature and datasets for conducting macroeconometric research.</li> <li>Perform independent research projects using macroeconomic data and econometric methods.</li> <li>Ability to implement empirical methods using the R programming language.  </li></ul> <ul> <li>Present and communicate research findings effectively through written reports, oral presentations, and visual displays.</li> <li>Communication and inter-personal skills required to work as a member of a group.  </li></ul> ProvisionalWeek 1: Introduction to Macroeconometrics<ul><li>Overview of the course and topics to be covered</li><li>The role of econometric methods in macroeconomics</li><li>Review of statistical and econometric concepts</li></ul>Week 2-3: Time Series Analysis and Forecasting<ul><li>Stationary and non-stationary time series models</li><li>ARIMA models and their applications</li><li>Vector autoregressive models and impulse response functions</li><li>Forecast evaluation and model selection</li></ul>Week 4-5: Bayesian Econometrics<ul><li>Introduction to Bayesian inference</li><li>Bayesian regression analysis</li><li>Markov Chain Monte Carlo methods</li><li>Applications to macroeconomic data and models</li></ul>Week 7-8: Structural Modeling in Macroeconomics<ul><li>Dynamic stochastic general equilibrium (DSGE) models</li><li>Estimation and inference in DSGE models</li><li>Applications to monetary and fiscal policy analysis</li><li>Model evaluation and robustness checks</li></ul>Week 10-11: Empirical Applications in Macroeconometrics<ul><li>Case studies and applications of macroeconometric techniques</li><li>Empirical research papers and presentations</li><li>Student research projects and presentations</li></ul>Week 12: Wrap-up and Review<ul><li>Review of key concepts and methods covered in the course</li><li>Final exam review and preparation</li></ul> <ul> <li>Synchronous activities (such as Lectures or Review and Q&A sessions, and tutorials), and guided self-study. </li> <li>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).</li></ul> 0 Other 100% Group work assessment - 20% Computing Test - 10% Midterm exam 30 minutes - 30% Final Exam - 40%  <ul> <li>Class feedback</li> <li>Office hours</li> <li>Revision sessions</li> <li>Discussion boards</li></ul> ECON10072A Advanced Statistics Pre-Requisite Compulsory ECON20110 Econometrics Pre-Requisite Compulsory ECON20532 Macroeconomic Analysis 4 Pre-Requisite Compulsory ECON20222 Quantitative Methods Pre-Requisite Compulsory ECON20032 Macroeconomics 4 Pre-Requisite Compulsory ECON10072B Advanced Statistics Pre-Requisite Compulsory ECON20072 Advanced Statistics Pre-Requisite Compulsory (ECON10072A OR ECON10072B OR ECON20072) AND (ECON20110 OR ECON20222) AND (ECON20532 OR ECON20032)(ECON10072 or ECON20072) and (ECON20110 or ECON20222) and (ECON20532 or ECON20032) Y Preliminary Reading ListTextbooks:<ol> <li>"Bayesian Econometric Methods," by Gary Koop (2003).</li> <li>"Bayesian Analysis of DSGE Models," by Edward P. Herbst and Frank Schorfheide (2015).</li> <li>"Time Series Analysis," by James D. Hamilton (1994).</li> <li>"Time Series Analysis: Forecasting and Control," by George E. P. Box, Gwilym M. Jenkins, and Gregory C. Reinsel (2015).</li></ol>Papers:<ol> <li>Bai, J., & Ng, S. (2002). "Determining the number of factors in approximate factor models." Econometrica, 70(1), 191-221.</li> <li>Sims, C. A. (1980). "Macroeconomics and reality." Econometrica, 48(1), 1-48.</li> <li>Engle, R. F. (1982). "Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation." Econometrica, 50(4), 987-1008.</li> <li>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.</li></ol>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. 0 0 174 Scheduled activity hoursStaff/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) 100Directed reading 74Total hours 200Independent study hoursPrivate study (Include seminar preparation, presentation preparation and essay preparation) 100Directed reading 74Additional notes Through their pre-requisites students are expected to have good working knowledge of the R programming language.