Bao Nguyen1
2024-08-16
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.
Students are expected to have a strong background in macroeconomics, econometrics, and mathematics. Familiarity with time-series analysis is recommended.
Mastering the core knowledge blocks, the foundation of macroeconomics.
Deep understanding of modern knowledge in the field of in-depth research in macroeconomics.
Conceptualizing and modeling real-world economic issues into research topics, and designing corresponding research methods.
Analysis, critique, and decision-making with expertise in the field of development economics and in-depth research.
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.
Kilian, L., & Lütkepohl, H. (2017). Structural vector autoregressive analysis. Cambridge University Press. Preliminary chapters can be found here.
Introduction
Overview of Macroeconomics and Macroeconometrics
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
Structural Vector Autoregressions: Introduction & Estimation
Kilian, L., & Lütkepohl, H. (2017). Structural vector autoregressive analysis. Cambridge University Press. Chapter 1, 2, 3, 4.
Introduction to R and R Markdown
Coghlan, A. (2015). A little book of R for time series. Published under Creative Commons Attribution, 3.
R Markdown Cookbook: https://bookdown.org/yihui/rmarkdown-cookbook/
Structural Vector Autoregressions: Identifications
Kilian, L., & Lütkepohl, H. (2017). Structural vector autoregressive analysis. Cambridge University Press. Chapter 8, 9, 10, 11, 12, 13, 14.
Midterm assignment: research proposal discussion
Bayesian VAR: Introduction
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
Structural Vector Autoregressions: Overview: Linear and Non-Linear models
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
Replication 1
Replication 2
Project presentations
Project presentations
Email: nguyenhoaibao@gmail.com↩︎