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Course Syllabus

Bachelor 2020/2021

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
Instructors: Madina Karamysheva, Oxana A. Malakhovskaya
Language: English
ECTS credits: 6
Contact hours: 64

Course Syllabus

Full Syllabus

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

  • 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
    (a) Time Series Data { Stochastic processes Stationary and Ergodic Processes (b) ARs, MAs and ARMA processes (c) Correlogram, forecasting, and lag length selection, Box-Jenkins approach
  • 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
    (a) Description of VAR models (estimation, impulse responses, variance decomposi- tion and forecasting) (b) Identication of VAR i. From VAR innovations to structural shocks ii. SVAR models: identication (short run, long run, sign restrictions) iii. Structural Shocks identied independently from VAR (c) Cointegration end Error Correction representation (ECM) (d) GVAR
  • VAR applications
    (a) Finance. Log-linearized Models of Stock and Bond Returns (b) Macro. Monetary policy (c) Macro. Fiscal policy
  • Modeling the conditional variance (ARCH, GARCH, Multivariate GARCH)
    (a) GARCH application: i. Value at Risk ii. Contagion
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)
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.2 * Big practical homework + 0.3 * Final test + 0.15 * Home assignments + 0.3 * Midterm test + 0.05 * Quizzes
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