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Магистратура 2020/2021

Анализ временных рядов

Статус: Курс по выбору (Финансовый аналитик)
Направление: 38.04.08. Финансы и кредит
Когда читается: 1-й курс, 3 модуль
Формат изучения: без онлайн-курса
Охват аудитории: для своего кампуса
Прогр. обучения: Финансовый аналитик
Язык: английский
Кредиты: 3

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

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

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

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

Assessment Elements

  • non-blocking time econometric research
  • non-blocking control work
  • non-blocking exam work
    The final test is scheduled for 26.06 at 18:30. Short description Time series and Econometrics exams are organized in written form with asyncronious proctoring. The exam problems will be available at the Google Forms platform: Econometrics: https://forms.gle/ZSQy7aK7XJC6xe476 Time series: https://forms.gle/NBcc3VjCtqpYsBBZ9 Proctoring will be provided at the Examus platform: https://hse.student.examus.net You should authentificate at the Examus platform 5 minutes before the exam. You should turn on your microphone and camera and prove your identity with passport. During the exam you can use one A4 cheat sheet and calculator. Critical values will be provided in the text of the exam. You are not allowed to google or to chat with other persons. Internet connection missing for more than 10 minutes is considered as a long-term loss of connection. In this case you will have a retake exam with the same rules. The lengh of the exam is 120 minutes. Check the requirements for your computer at https://elearning.hse.ru/data/2020/05/07/1544135594/Технические%20требования%20к%20ПК%20студента.pdf May the Force be with You!
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.2 * control work + 0.6 * exam work + 0.2 * time econometric research
Bibliography

Bibliography

Recommended Core Bibliography

  • Applied econometric time series, Enders W., 2004
  • 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
  • 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

  • Banerjee, A., Dolado, J. J., Galbraith, J. W., & Hendry, D. (1993). Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data. Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.oxp.obooks.9780198288107