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Economic Forecasting for Decision Making

  • Enseignant(s):   M.Grobéty  
  • Titre en français: Prévisions économiques pour la prise de décision
  • Cours donné en: anglais
  • Crédits ECTS: 6 crédits
  • Horaire: Semestre d'automne 2022-2023, 4.0h. de cours (moyenne hebdomadaire)
  •  séances
  • Formation concernée: Maîtrise universitaire ès Sciences en économie politique
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Economic forecasts are made and used in numerous decision-making situations in the real world. For instance, the governing board of a central bank such as the Swiss National Bank takes monetary policy measures based on its forecasts for the Swiss economy. The investment committee of a Swiss bank may need to know the economic outlook in China before rebalancing its portfolio to be more exposed to the Chinese market. Another example could be an export-oriented firm such as a Swiss watch company that hires and invests more today because its boards predict world growth to accelerate in the coming quarters.

The objective of the class is to equip students with the main concepts and methods used in applied economic forecasting. They will then apply these tools by managing a forecasting project aiming at guiding management decisions. The project will enable students to build knowledge of how to develop a forecasting model and to evaluate its performance using the statistical software R. They will also learn to effectively communicate their quantitative results to different types of audience (experts vs. decision makers) in order to maximize their impact in the decision-making process.


The class is divided in two parts. The first part consists of a lecture which presents the main concepts and methods used in applied economic forecasting. The outline of the lecture is:

1) Getting started

  1. Who forecasts, and why?
  2. Universal considerations for a forecaster

2) Toolbox for economic forecasting using time-series methods

  1. Optimal forecasts
  2. The components perspective of time series: cycles, trends and seasonality
  3. Main time series models used for economic forecasting: univariate models, multivariate models for small and large datasets
  4. Forecast combination
  5. Density forecasts

3) Development and practical use of forecasting models for decision making

  1. Point forecast evaluation
  2. Density forecast evaluation
  3. Communication
  4. Development of a forecasting model for decision making
  5. Some practical forecasting issues

In second part of the class, you will apply these concepts and methods by managing a forecasting project. The key objective of the project is to develop quantitative tools aiming at supporting a government, a central bank, an international organization, or a company in its decision-making process. Typically, a project involves three steps: (1) development of a forecasting model and evaluation of its performance; (2) production of a technical report (designed for experts) in which the data, the model and the results of the evaluation exercise are presented; (3) communication of your forecast to decision makers through a brief non-technical report and a short presentation. Different subjects will be proposed within the first weeks of the semester and a special attention will be given to the Swiss and Chinese economy. The project can be realized in a group from 1 to 3 students.


  • Ghysels, Eric and Marcellino, Massimiliano (2018), Applied economic forecasting using time series methods, Oxford University Press.
  • Diebold, F.X. (2017), Forecasting, Department of Economics, University of Pennsylvania.
  • Stock, James H and Watson, Mark W (2016), Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics, Handbook of macroeconomics, volume 2, pp. 415-525.
  • Hamilton, J. (1994), Time Series Analysis, Princeton University Press.


Good knowledge of the statistical software R and time series econometrics is required.


1ère tentative

Ecrit 1 heures

The final grade will be composed of:

  • the project taking place during the semester (2/3 of final grade): modeling, technical report, non-technical report and presentation.
  • a multiple-choice exam taking place during the exam session (1/3 of final grade).


Ecrit 2 heures

The integrative exam accounts for 100% of the final grade.

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