Advanced Macroeconomics

Bao Nguyen

2025-July

Meeting URL:

https://utas.zoom.us/j/88309197851?from=addon

Meeting ID:

883 0919 7851

Course Description

This course at the PhD level delves deeply into the methods and techniques employed in macroeconomic analysis and prediction. The objective of this course is to equip students with a thorough comprehension of macroeconomic analysis utilizing vector autoregression (VAR) models. The course especially covers the theoretical underpinnings of VAR models - the workhorse of empirical macroeconomics and finance. Students will learn estimation procedures, and their application to a range of macroeconomic problems. It also allows students to apply VAR techniques to actual data and cultivate the essential competencies to undertake their research of interest. Through out the course, exercises and replications will be using R language.

Prerequisites

Students are expected to have a strong background in macroeconomics, econometrics, and mathematics. Familiarity with time-series analysis is recommended.

Course Learning Outcomes

1. Mastering the core knowledge blocks, the foundation of macroeconomics.

2. Deep understanding of modern knowledge in the field of in-depth research in macroeconomics.

3. Conceptualizing and modeling real-world economic issues into research topics, and designing corresponding research methods.

4. Analysis, critique, and decision-making with expertise in the field of development economics and in-depth research.

Assessment

Students will be evaluated based on their performance on assignments and a final research project. The research project will involve applying VAR models to real-world macroeconomic problems and presenting the results in a research paper format. Research topics will be either chosen by students or given by the course coordinator. Details will be provided.

The course textbooks

Kilian, L., & Lütkepohl, H. (2017). Structural vector autoregressive analysis. Cambridge University Press. Preliminary chapters can be found here.

Topics

Session 1: Overview of Macroeconomics and Macroeconometrics (pdf)

• Blanchard, Olivier J. "Convergence? Thoughts about the evolution of mainstream macroeconomics over the last 40 years." (2025). download

• Robert Barro, The Old Keynesian Model, (2025). download

• Sargent, Thomas J. “Macroeconomics After Lucas.” (2024). download

• Glandon, P. J., Ken Kuttner, Sandeep Mazumder and Caleb Stroup. 2023. “Macroeconomic Research, Present and Past.” Journal of Economic Literature, 61 (3): 1088-1126. download

• Ramey, V. A. (2016). Macroeconomic shocks and their propagation. Handbook of macroeconomics, 2, 71-162. download

• Christiano, Lawrence J., Martin S. Eichenbaum, and Mathias Trabandt. "On DSGE models." Journal of Economic Perspectives 32.3 (2018): 113-140. download

• Giacomini, Raffaella. "The relationship between DSGE and VAR models." VAR models in macroeconomics–new developments and applications: Essays in honor of Christopher A. Sims (2013): 1-25 download

• Mankiw, N. Gregory. "Real Business Cycles: A New Keynesian Perspective: Journal of Economic Perspectives 3 (3), Summer 1989, pp. 79–90." Real Business Cycles. Routledge, 2013. 552-563. download

Suggested topics for your research project

Session 2: Structural Vector Autoregressions: Introduction & Estimation (pdf)

• Kilian, L., & Lütkepohl, H. (2017). Structural vector autoregressive analysis. Cambridge University Press. Chapter 1, 2, 3, 4.

Session 3: Introduction to R and R Markdown

• How to download and install R and Rstudio

• Coghlan, A. (2015). A little book of R for time series. Published under Creative Commons Attribution, 3.

• https://rmarkdown.rstudio.com/

• R Markdown: The Definitive Guide

• R Markdown Cookbook:

• Download data for replicating Blanchard & Quah's "The Dynamic Effects of Aggregate Demand and Supply Disturbances" (1989, AER) 

Session 4: Structural Vector Autoregressions: Identifications (HTML)

• Kilian, L., & Lütkepohl, H. (2017). Structural vector autoregressive analysis. Cambridge University Press. Chapter 8, 9, 10, 11, 12, 13, 14.

Session 5: Midterm assignment: research proposal discussion

Session 6: Bayesian VAR: Introduction (pdf)

• Kilian, L., & Lütkepohl, H. (2017). Structural vector autoregressive analysis. Cambridge University Press. Chapter 5.

• Joshua Chan (2020) In: P. Fuleky (Eds), Macroeconomic Forecasting in the Era of Big Data, 95-125, Springer, download

• Koop, G., & Korobilis, D. (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends® in Econometrics, 3(4), 267-358. download

Session 7: Linear and Non-Linear models (pdf)

• Kilian, L., & Lütkepohl, H. (2017). Structural vector autoregressive analysis. Cambridge University Press. Chapter 18.

• Nguyen, B. H., & Okimoto, T. (2019). Asymmetric reactions of the US natural gas market and economic activity. Energy Economics, 80, 86-99. download

• Hou, C., & Nguyen, B. H. (2018). Understanding the US natural gas market: A Markov switching VAR approach. Energy Economics, 75, 42-53. download

• Cross, J., & Nguyen, B. H. (2018). Time-varying macroeconomic effects of energy price shocks: A new measure for China. Energy Economics, 73, 146-160. download

Session 8: Replication 1

TBA

Session 9: Replication 2

TBA

Session 10 - 12: Project presentations and Wrap up