Master 2022/2023

Time Series Analysis

Category 'Best Course for New Knowledge and Skills'

Type: Elective course (Financial Analyst)

Area of studies: Finance and Credit

Delivered by: HSE Banking Institute

Where: HSE Banking Institute

When: 1 year, 3 module

Mode of studies: offline

Open to: students of one campus

Instructors: Boris Demeshev

Master’s programme: Financial Analyst

Language: English

ECTS credits: 3

Contact hours: 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 camp
  interactive exercises in phyton
• group project
  1-3 students, phyton
• final exam
  6 problems , 1-4 sheatseet

Interim Assessment

• 2022/2023 3rd module
  0.3 * 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