Change Point Detection, Inference & Relevance in the Context of Forecast Evaluations

Bertinoro, 20 - 25 July 2024



Francesco Ravazzolo

Free University of Bozen-Bolzano
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.


Basic knowledge of econometrics and statistics. 


Course description

The summer school offers a selective introduction to structural breaks, with a balance between theory and applications. 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 the relevant asymptotic theory; retrospective tests for breaks; real-time, online detection of breaks; in the presence of breaks, the estimation and construction of confidence intervals for break dates, and the estimation of the number of breaks; real-time detection of bubble phenomena. The ideas would be extended to the literature on forecast evaluation, considering tests of absolute and relative predictive ability, particularly in the presence of instabilities/breaks. The course would cover evaluation framework relevant for point predictions, as well as density calibration. The usefulness of the tests will be demonstrated by macroeconomic and forecasting applications


Reference textbooks for the course:

Reading List, Part 1:

  • Andrews D (1993) Tests for parameter instability and structural change with unknown change point. Econometrica 61:821-856
  • Aue A, Hormann S, Horvath L, Reimherr M (2009a) Break detection in the covariance structure of multivariate time series models. Annals of Statistics 37:4046-4087
  • Estimation of a change point in multiple regression models. The Review of Economics and Statistics 79:551-563
  • Bai J, Perron P (1998) Estimating and testing linear models with multiple structural changes. Econometrica 66:47-78
  • Chu CS, Stinchcombe M, White H (1996) Monitoring structural change. Econometrica 64(5):1045-65
  • Csorgo M, Horvath L (1997) Limit Theorems in Change-Point Analysis. Wiley, New York
  • Horvath L (1993) The maximum likelihood method for testing changes in the parameters of normal observations. The Annals of Statistics 21:671-680
  • Horvath L, Kokoszka P, Steinebach J (2007) On sequential detection of parameter changes in linear regression. Statistics & Probability Letters 77:885-895
  • Horvath L, Miller C, Rice G (2020) A new class of change point test statistics of Renyi type. Journal of Business & Economic Statistics 38(3):570-579
  • Horvath L, Trapani L (2022) Changepoint detection in heteroscedastic random coefficient autoregressive models. Journal of Business and Economic Statistics
  • Horvath L, Trapani L (2023) Real-time monitoring with RCA models. Mimeo

Reading List, Part 2:

  • Clark T, McCracken M (2013) Chapter 20 - Advances in Forecast Evaluation, Editor(s): Graham Elliott, Allan Timmermann, Handbook of Economic Forecasting, Elsevier, Volume 2, Part B, Pages 1107-1201.
  • Corradi V, Swanson N R (2006) Chapter 5 - Predictive Density Evaluation, Editor(s): G. Elliott, C.W.J. Granger, A. Timmermann, Handbook of Economic Forecasting, Elsevier, Volume 1, Pages 197-284.
  • Demetrescu M and Kruse-Becher R (2021) Is U.S. real output growth really non-normal? Testing distributional assumptions in time-varying location-scale models, CREATES Research Papers 2021-07, Department of Economics and Business Economics, Aarhus University.
  • Giacomini R and Rossi B (2010) Forecast comparisons in unstable environments. Journal of Applied Econometrics, 25: 595-620.
  • Hoesch L, Rossi B, Sekhposyan, T (2023) Has the Information Channel of Monetary Policy Disappeared? Revisiting the Empirical Evidence. American Economic Journal: Macroeconomics, 15 (3): 355-87.
  • Rossi B, Chapter 21 - Advances in Forecasting under Instability, Editor(s): Graham Elliott, Allan Timmermann, Handbook of Economic Forecasting, Elsevier, Volume 2, Part B, 2013, Pages 1203-1324.
  • Rossi B and Sekhposyan T (2016) Forecast Rationality Tests in the Presence of Instabilities, with Applications Federal Reserve and Survey Forecasts. Journal of Applied Econometrics, 31: 507–532.
  • Rossi B, Sekhposyan T (2013) Conditional predictive density evaluation in the presence of instabilities. Journal of Econometrics, 177 (2): 199-212.
  • Rossi B, Sekhposyan T (2019) Alternative tests for correct specification of conditional predictive densities. Journal of Econometrics, 208 (2): 638-657.
  • West, K D (1996) Asymptotic Inference about Predictive Ability. Econometrica, 64 (5): 1067–84.
  • West, K D and McCracken M W (1998) Regression-Based Tests of Predictive Ability. International Economic Review, 39 (4): 817–40


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 timetable:

  • Saturday to Wednesday: lectures 9:00-13:00, 15:00-17:00
  • Thursday: lectures 9:00-13:00, 15:00-16:00.


Schedule of the course:

  • Sat 20 Jul – lesson and tutorial (6h)
  • Sun 21 Jul – lesson and tutorial (6h)
  • Mon 22 Jul – lesson and tutorial (6h)
  • Tue 23 Jul – lesson and tutorial (6h)
  • Wed 24 Jul – lesson and tutorial (6h)
  • Thu 25 Jul – Student presentations.