Прогнозирование в экономике и финансах

2025/2026

Статус: Маго-лего

Кто читает: Банковский институт

Где читается: Банковский институт

Когда читается: 3 модуль

Охват аудитории: для своего кампуса

Язык: английский

Кредиты: 3

Контактные часы: 28

Full Syllabus Ask Question

Abstract

The course is an introduction to main forecasting techniques used in economics and finance. It covers topics ranging from data collection and preparation to econometrics, general equilibrium and machine learning models used in forecasting. This course is mostly practical, not theoretical, so a significant amount of time will be devoted to application of the models discussed to real data.

Learning Objectives

The main aim of the course is to provide the students with understanding of how the forecasting is usually conducted. It includes both the ability to use and evaluate external forecasts and the ability to make forecasts themselves. Students should be able to find the data they need, choose the model suitable for a certain problem, evaluate the forecasting performance of the model and interpret the results obtained. Apart from that, application of forecasting to decision making process will be discussed.

Expected Learning Outcomes

After the course students are to be able to perform all the necessary forecasting steps using the basic set of models: data collection and preparation, model selection, forecast evaluation. For a wider range of more complicated models students are expected to be able to understand and assess pre-build models
After the course, students should be able to find the data they need, choose the model suitable to a certain problem, evaluate the forecasting performance of the model and interpret the results obtained. Apart from that, application of forecasting to decision-making process will be discussed.

Course Contents

Sources of economic and financial data and external forecasts
Main Macroeconomic Indicators. Data collection and preparation, outliers, seasonal adjustment
Measures of forecasting performance
Time series econometrics models: stationary and non-stationary time series, ARIMA
Regression analysis
Time series econometrics models: ARCH model and its specification, ARCH-types models
Vector autoregression, Bayesian Vector autoregression
Backcasting
Panel data

Assessment Elements

midterm assessment
Midterm assessment can be conducted in the form of a written assignment, with restrictions on the use of any supportive materials (except permitted scripts).
Group project
The group project is an analytical report that includes model construction, calculations based on real data, analysis and forecasting.
Final exam
Final exam can be conducted in the form of a written/verbal assignment, with restrictions on the use of any supportive materials. Final exam result is the blocking exam (that is blocking element of assessment).
in-class tests
Each class starts with a test based on the completed previous materials (except for the first class)

Interim Assessment

2025/2026 3rd module
0.4 * Final exam + 0.28 * Group project + 0.12 * in-class tests + 0.2 * midterm assessment

Bibliography

Recommended Core Bibliography

Chou, R. Y. (2005). Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model. Journal of Money, Credit and Banking, (3), 561. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.mcb.jmoncb.v37y2005i3p561.82
Enders, W. (2015). Applied Econometric Time Series (Vol. Fourth edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1639192
Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and Applications. Cyprus, Europe: John Wiley & Sons, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.F848CE7

Recommended Additional Bibliography

Paweł Kaczmarczyk. (2020). Feedforward Neural Networks and the Forecasting of Multi-Sectional Demand for Telecom Services : a Comparative Study of Effectiveness for Hourly Data. Acta Scientiarum Polonorum. Oeconomia, 8(3), 13–25. https://doi.org/10.22630/ASPE.2020.19.3.24

Authors

ELIZAROVA IRINA NIKOLAEVNA
ODINTSOVA ULYANA ALEKSANDROVNA

Course Syllabus