Master
2024/2025
Forecasting in Economics and Finance
Type:
Elective course (Financial Analyst)
Area of studies:
Finance and Credit
Delivered by:
HSE Banking Institute
Where:
HSE Banking Institute
When:
1 year, 3 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Маркина Вероника Сергеевна
Master’s programme:
Financial Analyst
Language:
English
ECTS credits:
3
Course Syllabus
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
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
- Regression analysis
- Time series econometrics models: ARIMA
- Time series econometrics models: ARCH model and its specification, ARCH-types models
- Vector autoregression, Bayesian Vector autoregression
- Macroeconomic models: general equilibrium models
- Basic machine learning techniques: LASSO, decision trees
- Scenario forecasting
- Policy implications of forecasts
Assessment Elements
- Final examFinal exam can be conducted in the form of a written/verbal assignment, with restrictions on the use of any supportive materials.
- Home assignmentsHome assignments based on completed materials and have the submission deadline, this element of control cannot be retaken. Home assignments consist of certain calculations based on the considered models.
- Group projectGroup project has the submission deadline, this element of control cannot be retaken. It is performed based on the following criteria: completeness of the answer, logic of the answer, the depth of information provided, the correctness of calculations and the quality of analysis provided. The group project is an analytical report that includes model construction, calculations based on real data, analysis and forecasting.
- In-class testsEach class starts with a test based on the completed previous materials (except for the first class).
Interim Assessment
- 2024/2025 3rd module0.2 * Final exam + 0.4 * Group project + 0.25 * Home assignments + 0.15 * In-class tests
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
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