Магистратура
2022/2023
Анализ временных рядов
Лучший по критерию «Новизна полученных знаний»
Статус:
Курс по выбору (Финансовый аналитик)
Направление:
38.04.08. Финансы и кредит
Кто читает:
Банковский институт
Где читается:
Банковский институт
Когда читается:
1-й курс, 3 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Демешев Борис Борисович
Прогр. обучения:
Финансовый аналитик
Язык:
английский
Кредиты:
3
Контактные часы:
28
Course Syllabus
Abstract
Time Series Analysis (Master level) is an elective course designed for the first year Master students of “Finantial Analytic” Program. This is an intermediate course of Time Series Theory for the students specializing in the field of Finance and Banking. The course is taught in English.The stress in the course is made on the sense of facts and methods of time series analysis. Conclusions and proofs are given for some basic formulas and models; this enables the students to understand the principles of economic theory. The main stress is made on the economic interpretation and applications of considered economic models.
Learning Objectives
- The students should get acquainted with the main concepts of Time Series theory and methods of analysis. They should know how to use them in examining financial processes and should understand methods, ideas, results and conclusions that can be met in the majority of books and articles on economics and finance. In this course, students should master traditional methods of Time Series analysis, intended mainly for working with time series data. Students should understand the differences between cross-sections and time series, and those specific economic problems, which occur while working with data of these types
Expected Learning Outcomes
- Students should become skillful in analysis and modelling of stochastic processes of ARMA (p, d, q) models, get acquainted with co-integration and error correction models, autoregressive models with distributed lags, understand their application in economics. Considered methods and models should be mastered by practice using real economic data and modern economic software Econometric views and R
Course Contents
- Stochastic process and its main characteristics
- Autoregressive-moving average models ARMA (p,q). Estimation of coefficients of ARMA (p,q) processes. Box-Jenkins’ approach
- Forecasting in Box-Jenkins model
- Non-stationary time series
- Unit root problems. Unit root and structure changes
- Regressive dynamic models
- Vector autoregressive model and co-integration
- Causality in time series
Assessment Elements
- Assign on data campinteractive exercises in phyton
- group project1-3 students, phyton
- final exam6 problems , 1-4 sheatseet
Interim Assessment
- 2022/2023 3rd module0.3 * group project + 0.5 * final exam + 0.2 * Assign on data camp
Bibliography
Recommended Core Bibliography
- 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
- Whang,Yoon-Jae. (2019). Econometric Analysis of Stochastic Dominance. Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.cup.cbooks.9781108472791
Recommended Additional Bibliography
- Michael Beenstock, & Daniel Felsenstein. (2019). The Econometric Analysis of Non-Stationary Spatial Panel Data. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.adspsc.978.3.030.03614.0