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Regular version of the site
Bachelor 2022/2023

Time Series and Stochastic Processes

Category 'Best Course for New Knowledge and Skills'
Type: Compulsory course (Data Science and Business Analytics)
Area of studies: Applied Mathematics and Information Science
When: 3 year, 1-4 module
Mode of studies: distance learning
Online hours: 10
Open to: students of one campus
Language: English
ECTS credits: 8
Contact hours: 120

Course Syllabus

Abstract

This course is conducted at Data Science and Business Analytics program and is provided to 3rd-year undergraduates who have studied a course covering basic probability and statistical inference. A half of this course introduces concepts of Markov chains, random walks, martingales as well as of to the time series. The course requires basic knowledge in probability theory and linear algebra. It introduces students to the modeling, quantification and analysis of uncertainty. The main objective of this course is to develop the skills needed to do empirical research in fields operating with time series data sets. The course aims to provide students with techniques and receipts for estimation and assessment of quality of economic models with time series data. The course will also emphasize recent developments in Time Series Analysis and will present some open questions and areas of ongoing research.
Learning Objectives

Learning Objectives

  • The main objective of this course is to develop the skills needed to do empirical research in fields operating with time-series data sets.
  • The course will emphasize recent developments in Time Series Analysis and will present some open questions and areas of ongoing research.
Expected Learning Outcomes

Expected Learning Outcomes

  • Students get an understanding of techniques and receipts for estimation and assessment of the quality of economic models with time-series data.
Course Contents

Course Contents

  • Discrete-time martingale theory.
  • Continuous-time stochastic processes.
  • Stochastic calculus and differential equations.
  • Continuous-time financial models.
  • Autoregressive-moving average models ARMA (p,q) Moving average models МА(q). Condition of invertibility. Autoregressive models АR(р). Yull-Worker equations. Stationarity conditions. Autoregressive-moving average models ARMA (p,q). Coefficient estimation in ARMA (p,q) processes. Box-Jenkins’ approach Coefficients estimation in autoregressive models. Coefficient estimation in ARMA (p) processes.
  • Forecasting in the framework of Box-Jenkins model. Forecasting, trend and seasonality in Box-Jenkins model.
  • Non-stationary time series, TSP or DSP: methodology of research. Segmented trends and structure changes
  • Regressive dynamic models. Autoregressive models with distributed lags (ADL).
  • Vector autoregression model and co-integration.
  • Time series co-integration. Co-integration regression. Testing of co-integration. Vector autoregression and co-integration. Co-integration and error correction model.
Assessment Elements

Assessment Elements

  • non-blocking Home Assignment 1st module
  • non-blocking Fall midterm
  • non-blocking Home Assignment 2-4 module
  • non-blocking Winter midterm
  • non-blocking Spring midterm
  • non-blocking Final exam
Interim Assessment

Interim Assessment

  • 2022/2023 1st module
    0.7 * Fall midterm + 0.3 * Home Assignment 1st module
  • 2022/2023 4th module
    Module 1 score*0.2+HA (m2-m4)*0.25+Winter Midterm*0.15+Spring Midterm*0.25+Final Exam0.15
Bibliography

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
  • Harvey, A. C. (1993). Time Series Models (Vol. 2nd ed). Cambridge, Mass: MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=11358
  • Mills, T. C., & Markellos, R. N. (2008). The Econometric Modelling of Financial Time Series: Vol. 3rd ed. Cambridge University Press.

Recommended Additional Bibliography

  • Bartoszyński, R., & Niewiadomska-Bugaj, M. (2008). Probability and Statistical Inference (Vol. 2nd ed). Hoboken, N.J.: Wiley-Interscience. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=219782
  • Freund, J. E., Miller, I., & Miller, M. (2014). John E. Freund’s Mathematical Statistics with Applications: Pearson New International Edition (Vol. Eighth edition, Pearson new international edition). Essex, England: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1418305
  • Hogg, R. V., Zimmerman, D. L., & Tanis, E. A. (2015). Probability and Statistical Inference, Global Edition (Vol. Ninth edition. Global edition). Boston: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1419274
  • Larsen, R. J., & Marx, M. L. (2015). An introduction to mathematical statistics and its applications. Slovenia, Europe: Prentice Hall. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.19D77756
  • Lindgren, B. W. (1993). Statistical Theory (Vol. Fourth edition). Boca Raton, Florida: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1683924

Authors

  • Галевская Софья Андреевна
  • LUKYANCHENKO PETR PAVLOVICH