Прогнозирование в экономике и финансах

2026/2027

Статус: Маго-лего

Кто читает: Банковский институт

Где читается: Банковский институт

Охват аудитории: для своего кампуса

Язык: русский

Кредиты: 3

Контактные часы: 28

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Аннотация

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.

Цель освоения дисциплины

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.

Планируемые результаты обучения

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.

Содержание учебной дисциплины

Sources of economic and financial data and external forecasts
Main Macroeconomic Indicators. Data collection and preparation, outliers, seasonal adjustment
Measures of forecasting performance
Exponential smoothing
Time series econometrics models: stationary and non-stationary time series, 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
Main Macroeconomic Indicators
Regression analysis
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
Backcasting
Panel data

Элементы контроля

Group project
The group project is an analytical report that includes model construction, calculations based on real data, analysis and forecasting.
in-class tests
Each class starts with a test based on the completed previous materials (except for the first class)
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).
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).

Промежуточная аттестация

2026/2027 3rd module
0.4 * Final exam + 0.12 * in-class tests + 0.2 * midterm assessment + 0.28 * Group project

Список литературы

Авторы

Елизарова Ирина Николаевна
Ужегов Алексей Александрович

Программа дисциплины