2023/2024
Прогнозирование в экономике и финансах
Статус:
Маго-лего
Кто читает:
Банковский институт
Когда читается:
3 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Ужегов Алексей Александрович
Язык:
английский
Кредиты:
3
Контактные часы:
28
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
- Data collection and preparation, outliers, seasonal adjustment
- Measures of forecasting performance
- Exponential smoothing
- Time series econometrics models: ARIMA
- Time series econometrics models: ADL
- Forecast report. Results presentation and visualization
- Scenario forecasting
- Policy implications of forecasts
- Overview of advanced Time series econometrics models
Assessment Elements
- HomeworkPractical skills will be assessed by a written essay performed using real-world data and R software. Written exam will assess theoretical knowledge and general understanding of the practical side of the subject. Homework requires practical skills in R obtained during the course. An essay should be written based on the time series analysis done by student, which includes key components of forecast report: data preparation, model analysis, visualization, and interpretation of the results.
- ExamWritten exam in a form of test
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