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Course Syllabus

Bachelor 2021/2022

Times Series Econometrics

Type: Elective course (HSE/NES Programme in Economics)
Area of studies: Economics
Delivered by: School of Finance
Where: Faculty of Economic Sciences
When: 4 year, 1, 2 module
Mode of studies: offline
Open to: students of one campus
Instructors: Madina Karamysheva, Oxana A. Malakhovskaya
Language: English
ECTS credits: 6
Contact hours: 64

Course Syllabus

Full SyllabusAsk Question

Abstract

We first review the basics of time series econometrics. Then, in more details, we look at the VAR class of models, including VAR, VARX, VECM, GVAR, and its rather broad application to macroeconomics, including fiscal and monetary policy and some finance applications. After that, we cover ARCH, GARCH with its application to value at risk and contagion. Course Prerequesites: Linear Algebra, Probability Theory, Mathematical Analysis, Basic Econometrics
Learning Objectives

Learning Objectives

  • The objective of this course is to provide the student with tools for empirical analysis of time series and to show how econometric models can be applied to empirical models in macroeconomics and finance.
  • to provide the student with tools for empirical analysis of time series and to show how econometric models can be applied to empirical models in macroeconomics and finance
Expected Learning Outcomes

Expected Learning Outcomes

  • Apply econometric models to empirical models in macroeconomics and finance
Course Contents

Course Contents

  • Introduction/reviewing of time series econometrics
  • Non-stationarity: trends (deterministic and stochastic) and unit root tests: conse- quences, detection, remedies, breaks
  • ARIMA Processes, Trend-cycle decompositions (Beveridge-Nelson, Hodrik-Prescott)
  • Multivariate Time Series Models. VAR
  • VAR applications
  • Modeling the conditional variance (ARCH, GARCH, Multivariate GARCH)
Assessment Elements

Assessment Elements

  • non-blocking Quizzes
  • non-blocking Home assignments
  • non-blocking Big practical homework
    big practical homework in the end of the course
  • non-blocking Midterm test
    (only if the grade is higher than Final).
  • non-blocking Final test
    (if the grade of midterm is higher than the nal grade) and 60% other wise. Please keep in mind that if a student receives a failing grade for a course, he or she gets two chances for a make-up. The rst make up is usually a retake (retake is similar to the nal test). This make-up is graded by the course instructor. The second make-up is graded by a committee consisting of three or more members, including the course instructor. It is important to notice, that the formula for the course grade does not change. So if you do not take part in any assignments, quizzes and you get zero, then your maximum grade will be 0*0.05 + 0*0.15 + 0.*0.2 + 0.6*(grade of retake)
  • non-blocking Quizzes
  • non-blocking Home assignments
  • non-blocking Big practical homework
    big practical homework in the end of the course
  • non-blocking Midterm test
    (only if the grade is higher than Final).
  • non-blocking Final test
    (if the grade of midterm is higher than the nal grade) and 60% other wise. Please keep in mind that if a student receives a failing grade for a course, he or she gets two chances for a make-up. The rst make up is usually a retake (retake is similar to the nal test). This make-up is graded by the course instructor. The second make-up is graded by a committee consisting of three or more members, including the course instructor. It is important to notice, that the formula for the course grade does not change. So if you do not take part in any assignments, quizzes and you get zero, then your maximum grade will be 0*0.05 + 0*0.15 + 0.*0.2 + 0.6*(grade of retake)
Interim Assessment

Interim Assessment

  • 2021/2022 2nd module
    0.3 * Midterm test + 0.05 * Quizzes + 0.2 * Big practical homework + 0.3 * Final test + 0.15 * Home assignments
Bibliography

Bibliography

Recommended Core Bibliography

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

Recommended Additional Bibliography

  • Bruce E. Hansen. (2001). The New Econometrics of Structural Change: Dating Breaks in U.S. Labour Productivity. Journal of Economic Perspectives, (4), 117. https://doi.org/10.1257/jep.15.4.117
  • Cochrane, J. H. (1994). Permanent and Transitory Components of GNP and Stock Prices. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.C46CF1D7
  • Galí, J. (1996). Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations? CEPR Discussion Papers. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.p.cpr.ceprdp.1499
  • Marianne Baxter, & Robert G. King. (1999). Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series. The Review of Economics and Statistics, (4), 575. https://doi.org/10.1162/003465399558454
  • Sims, C. A., Stock, J. H., & Watson, M. W. (1990). Inference in Linear Time Series Models with Some Unit Roots. Econometrica, (1), 113. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.ecm.emetrp.v58y1990i1p113.44

Authors

  • KARAMYSHEVA MADINA RINATOVNA