Embedded Files
Lectures will be Monday to Friday from 9am - 3pm, for a total of 20 lecture hours.
Participants will also have the opportunity to schedule bilateral meetings with the lecturer.
A social dinner will be organised on Thursday evening.
Day 1: Why Bayesian VARs?
Day 1: Why Bayesian VARs?
We compare the benefits of local projections and vector autoregressions as alternative econometric frameworks for conducting impulse response analysis to quantify the dynamic causal effects of macroeconomic shocks. We also review Bayesian principles and priors for the estimation of reduced-form VARs models.
Baumeister, C. (2026), “Comment on ‘Local Projections or VARs? A Primer for Macroeconomists’ by Montiel Olea, Plagborg-Møller, Qian, and Wolf,” NBER Macroeconomics Annual.
De Graeve, F. and A. Westermark (2025), “Long-Lag VARs,” Journal of Monetary Economics 156, article 103831.
Hamilton, J.D. (2026), Vector Autoregressions, Cambridge University Press, Chapters 1-3.
Ludwig, J. (2024), “Local Projections are VAR Predictions of Increasing Order,” mimeo, Texas Tech University.
Montiel Olea, J.L. Montiel Olea, M. Plagborg-Møller, E. Qian, and C.K. Wolf (2026), “Local Projections or VARs? A Primer for Macroeconomists,” NBER Macroeconomics Annual.
Day 2: Identification of Structural VAR models
Day 2: Identification of Structural VAR models
We revisit the identification problem in structural VAR models and introduce a general Bayesian framework that nests traditional identification schemes. In particular, we question the current practice of identification in VAR models using inequality constraints. We illustrate the problems that arise from the traditional algorithm based on sign restrictions with an application to modeling the labor market. We introduce a more flexible approach for estimation and inference that is not subject to these concerns.
Baumeister, C., and J.D. Hamilton (2015), “Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information,” Econometrica, 83(5), 1963-1999.
Baumeister, C., and J.D. Hamilton (2020), “Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions,” Journal of International Money and Finance, 109, article 102250.
Baumeister, C., and J.D. Hamilton (2024), “Advances in Using Vector Autoregressions to Estimate Structural Magnitudes,” Econometric Theory, 40(3), 472-510.
Hamilton, J.D. (2026), Vector Autoregressions, Cambridge University Press, Chapters 11 and 12.
Brinca, P., J.B. Duarte, and M. Faria-e-Castro (2021), “Measuring Labor Supply and Demand Shocks during COVID-19,” European Economic Review, 139, article 103901.
Rubio-Ramirez, J.F., D.F. Waggoner, and T. Zha (2010), “Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference,” Review of Economic Studies, 77(2), 665-696.
Day 3: The Role of Prior Information
Day 3: The Role of Prior Information
We illustrate this new method for identification by revisiting the role of oil supply and demand shocks in generating historical fluctuations in the price of oil and highlight several shortcomings of traditional approaches to identification of oil supply and demand shocks with a particular focus on the estimation of behavioral elasticities.
Baumeister, C., and J.D. Hamilton (2019), “Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks,” American Economic Review, 109(5), 1873-1910.
Baumeister, C., and J.D. Hamilton (2022), “Structural Vector Autoregressions with Imperfect Identifying Information,” AEA Papers and Proceedings, 112, 466-470.
Baumeister, C., and J.D. Hamilton (2024), “Advances in Vector Autoregressions to Estimate Structural Magnitudes,” Econometric Theory 40(3), 472-510.
Caldara, D., M. Cavallo, and M. Iacoviello (2016), “Oil Price Elasticities and Oil Price Fluctuations,” Journal of Monetary Economics, 103, 1-20.
Kilian, L. (2009). “Not all Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market,” American Economic Review, 99, 1053-1069.
Kilian, L., and D.P. Murphy (2012), “Why Agnostic Sign Restrictions Are Not Enough: Understanding the Dynamics of Oil Market VAR Models,” Journal of the European Economic Association, 10(5), 1166-1188.
Day 4: Inference in Set-Identified SVAR Models
Day 4: Inference in Set-Identified SVAR Models
We show how to supplement prior information about structural parameters with prior knowledge about the impacts of structural shocks. We apply this idea to the study of the dynamic effects of monetary policy, its role in business cycle fluctuations, and the estimation of structural policy response coefficients in an interest-rate rule. We also show how to construct credibility sets for impulse response functions, variance decompositions, and historical decompositions.
Baumeister, C., and J.D. Hamilton (2018), “Inference in Structural Vector Autoregressions When the Identifying Assumptions are Not Fully Believed: Re-evaluating the Role of Monetary Policy in Economic Fluctuations,” Journal of Monetary Economics, 100, 48-65.
Belongia, M.T., and P.N. Ireland (2021), “A Classical View of the Business Cycle,” Journal of Money, Credit, and Banking, 53(2-3), 333-366.
Read, M. (2024), “Set-identified Structural Vector Autoregressions and the Effects of a 100 Basis Point Monetary Policy Shock,” Review of Economics and Statistics, forthcoming.
Watson, M.W. (2019), “Comment on ‘On the Empirical (Ir)Relevance of the Zero Lower Bound’ by Debortoli, Gali, and Gambetti,” NBER Macroeconomics Annual.
Day 5: Recent Developments
Day 5: Recent Developments
We illustrate how to leverage the Bayesian structural VAR framework to formally evaluate the validity and relevance assumptions underlying the use of external instruments. We also show how to incorporate information about the long run and how to formulate priors in larger systems.
Aastveit, K.A., Bjørnland, H.C., and Cross, J.L. (2023), “Inflation Expectations and the Pass-through of Oil Prices”, Review of Economics and Statistics 105(3), 733-743.
Ahn, H.J., and J.B. Rudd (2025), “(Re-)Connecting Inflation and the Labor Market: A Tale of Two Curves,” Journal of Monetary Economics 153, article 103796.
Hamilton, J.D. (2026), Vector Autoregressions, Cambridge University Press, Chapter 9.
Nguyen, L. (2025), “Bayesian Inference in Proxy SVARs with Incomplete Identification: Re-evaluating the Validity of Monetary Policy Instruments,” Journal of Monetary Economics, 103813.
About the lecturer:
About the lecturer:
Professor Christiane Baumeister is a renowned expert in empirical macroeconomics, specializing in Bayesian inference in structural vector autoregression models. She is a professor and the associate chair of the Department of Economics at the University of Notre Dame. Her work has been published in leading journals such as Econometrica, the American Economic Review, and the Journal of Monetary Economics. She is also a research associate at the National Bureau of Economic Research (NBER) and a research fellow at the Centre for Economic Policy Research (CEPR). Professor Baumeister has extensive experience teaching advanced econometrics and Bayesian time series analysis. She has taught PhD-level courses in these subjects at institutions such as the Université Catholique de Louvain, BI Norwegian Business School, and DIW Berlin. She has also taught specialized courses at central banks, including the ECB, the Bank of Canada, and the Central Bank of Ireland. She co-authored a seminal and widely cited paper on Bayesian vector autoregressions with James Hamilton, considered foundational in the field, and she regularly makes methodological contributions to empirical macroeconomics.
Page updated
Google Sites
Report abuse