Methods in Time Series Econometrics

Bertinoro, 24 - 29 July 2023



Francesco Ravazzolo

Free University of Bozen-Bolzano
4 Universitätsplatz 1 - piazza Università, 1
39100 Bozen-Bolzano




The course is conditional to the recruitment of a minimum of 15 participants in presence. The maximum number of allowed participants in presence is 30.

If the conditions of the ongoing COVID 19 pandemic do not allow an in presence event, the course will be cancelled.

Basic knowledge of econometrics and statistics. 

Course description

The summer school offers a selective introduction to program evaluation methods in econometrics. The focus will be mostly on methodological developments, but applications will also be discussed as necessary. It would be ideal if participants had elementary working knowledge of statistics and econometrics at the master level, but the lectures will be self-contained. Topics covered include an introduction to Bayesian methods, Markov-switching VAR and Markov-switching DSGE, Textual Analysis, forecasting with machine learning techniques, Structural time series representations,  Recent advances in the estimation of dynamic causal effects, Identification and inference for impulse responses, Regressions in impulse response space, Policy counterfactuals and Optimal policy perturbations with impulse responses.


Schedule of the course:

• Mon 24-Jul – Introduction to Bayesian methods, Gibbs sampling, Metropolis Hastings. (6h)

• Tue 25-Jul – Markov-switching VAR, Markov-switching DSGE, Textual Analysis. (6h)

• Wed 26-Jul – Forecasting with machine learning (morning 2h) , Structural Time Series Representations and Estimation of Dynamic Causal Effects (morning 2h), and Student Presentations (afternoon 2h).

• Thu 27-Jul – Identification and Inference for Impulse Responses (6h).

• Fri 28-Jul – Regressions in Impulse Response Space, Policy Counterfactuals and Optimal Policy Perturbations (6h)

• Sat 29-Jul – Student presentations (morning 4h).


Reference textbooks for the course:

Reading List, Part 1:

  • Bianchi, F. (2013). Regime Switches, Agents’ Beliefs, and Post-World War II U.S. Macroeconomic Dynamics. Review of Economic Studies 80 (2), 463-490.
  • Bianchi, F. and C. Ilut (2017). Monetary/Fiscal Policy Mix and Agents’ Beliefs. Review of Economic Dynamics 26, 113-139.
  • Bianchi, F., C. Ilut, and M. Schneider (2018). Uncertainty Shocks, Asset Supply and Pricing over the Business Cycle. Review of Economic Studies 85 (2), 810-854.
  • Bianchi, F., S. C. Ludvigson, and S. Ma (2021). Belief distortions and macroeconomic Fluctuations, American Economic Review.
  • Bianchi, F. and L. Melosi (2017). Escaping the Great Recession. American Economic Review 107 (4), 1030-58.
  • Geweke, J. F. (2005). Contemporary Bayesian Econometrics and Statistics. New York: Wiley. (selected chapters)
  • Kim, C.-J. and C. R. Nelson (1999). State-Space Models with Regime Switching. Cambridge, Massachusetts: MIT Press. (selected chapters)
  • Lubik, T. and F. Schorfheide (2004). Testing for Indeterminacy: An Application to U.S. Monetary Policy. American Economic Review 94 (1), 190-217.
  • Sims, C. A. and T. Zha (2006). Were There Regime Switches in US Monetary Policy? American Economic Review 91 (1), 54-81.


Reading List, Part 2:

  • Barnichon, Regis and Geert Mesters (2020). “Identifying Modern Macro Equations with Old Shocks”. In: The Quarterly Journal of Economics 135.4
  • Barnichon, Regis and Geert Mesters (2022). A Sufficient Statistics Approach for Macro Policy Evaluation. Working Paper 2022-15. Federal Reserve Bank of San Francisco
  • Kilian, Lutz and Helmut Lutkepohl (2017). Structural Vector Autoregressive Analysis. Themes in Modern Econometrics. Cambridge University Press.
  • McKay, Alisdair and Christian K Wolf (2022). What Can Time-Series Regressions Tell Us About Policy Counterfactuals? Working Paper 30358. National Bureau of Economic Research.
  • Lewis, Daniel and Karel Mertens (2022). A Robust Test for Weak Instruments with Multiple Endogenous Variables, Working Paper 2208. Federal Reserve Bank of Dallas
  • Lewis, Daniel and Karel Mertens (2023). Dynamic Identification Using System Projections and Instrumental Variables. Working Paper 2204. Federal Reserve Bank of Dallas
  • Mertens, K and Morten O.R, 2013, The Dynamic Effects of Personal and Corporate Income Tax Changes, American Economic Review 103 (4)
  • Montiel Olea, Jose L., James H. Stock, and Mark W. Watson (2021). “Inference in Structural Vector Autoregressions identified with an external instrument”. In: Journal of Econometrics 225.1
  • Montiel Olea, Jose and Plagborg-Moller, M, Local Projection Inference is Simpler and More Robust Than You Think (with José Luis Montiel Olea). Econometrica 89(4), 2021, 1789-1823.
  • Plagborg-Moller, M and Christian Wolf, 2021, Local Projections and VARs Estimate the Same Impulse Responses, Econometrica 89 (2)
  • Ramey, V.A. (2016). “Chapter 2 - Macroeconomic Shocks and Their Propagation”. In: ed. by John B. Taylor and Harald Uhlig. Vol. 2. Handbook of Macroeconomics.
  • Stock, James H. and Mark W. Watson, 2018 , Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments”. The Economic Journal 128.610


Handouts, readings and further material will be provided before the beginning of the course and during the lectures. 


Venue and timetables

The Module will last one week and will be held in the University Residential Centre, Via Frangipane 6, 47032 Bertinoro (FC). Participants will be hosted in the Centre guest quarters (in case of reduced availability of rooms in the Centre, they will be accommodated in local hotels).
Lectures and tutorials will be in English, with the following schedule:

  • Monday to Friday: lectures 9:00-13:00, 15:00-17:00
  • Saturday: lectures 9:00-13:00.