Forecasting

  • Credits: 6
  • Ending: Examination
  • Range: 2P + 2C
  • Semester: winter
  • Year: 2
  • Faculty of Economics and Finance

Teachers

Included in study programs

Applied Economics

Teaching results

The course is focused on the use of forecasting methods used by analysts in the public and private sectors. It includes the practical application of one-dimensional and multidimensional analysis of time series of macroeconomic variables, as well as the system of structural equations. Selected topics will be delivered by experts from practice.
After completing this course students will:
a) have knowledge of forecasting methods in the private sector and public institutions
b) Skills: students will be able to make a forecast in the most used software packages (Stata, R) and present its results
c) Competences: students will be able to select and use an appropriate forecasting method given the nature of the data and the type of problem

Indicative content

Practical application of time series models and structural models based on theoretical knowledge from advanced econometrics.
1. Introduction. Principles and foundations of forecasting.
2. Forecasting in the private sector and state institutions. Qualitative methods.
3. Various approaches to forecasting and planning in budgeting.
4. Nowcasting, data type, de-trending and seasonal adjustment - X11, SEATS, STL
5. Models with autoregressive terms and moving averages of random components.
6. Hierarchical time series forecasting
7. VAR models, their limits for forecasting. Reduced and structural VAR models.
8. Dynamic factor models
9. Medium-term forecast horizon, ECM model, long-term trends and closing the gap.
10. State space representation, Kalman filter.
11. IMF GAP model of general equilibrium.
12. Evaluation and selection of the model.

Support literature

HYNDMAN, R.J. –¬ ATHANASOUPULOS, G. Forecasting: Principles and Practice, 3rd ed., 2021
LÜTKEPOHL, H. New introduction to multiple time series analysis. Springer Science & Business Media, 2005.
STOCK, J. H. ¬– WATSON, M. W. Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics. In: Handbook of macroeconomics. Elsevier, 2016. p. 415-525.
KAMENIK, O. et al. A Small Quarterly Projection Model of the US Economy. International Monetary Fund, 2008.

Requirements to complete the course

20 % coursework, 20 % assignments, 60 % final exam

Student workload

156 (participation in lectures 26, participation in seminars 26, preparation for seminars 13, elaboration of semester project 13, assignments elaboration 26, preparation for the final exam 52)

Language whose command is required to complete the course

English,

Date of approval: 12.03.2024

Date of the latest change: 04.03.2022