Бакалавриат
2021/2022
Эконометрика временных рядов
Статус:
Курс по выбору (Совместная программа по экономике НИУ ВШЭ и РЭШ)
Направление:
38.03.01. Экономика
Кто читает:
Школа финансов
Где читается:
Факультет экономических наук
Когда читается:
4-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
6
Контактные часы:
64
Course 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
- 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
- Apply econometric models to empirical models in macroeconomics and finance
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
- Quizzes
- Home assignments
- Big practical homeworkbig practical homework in the end of the course
- Midterm test(only if the grade is higher than Final).
- 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)
- Quizzes
- Home assignments
- Big practical homeworkbig practical homework in the end of the course
- Midterm test(only if the grade is higher than Final).
- 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
- 2021/2022 2nd module0.3 * Midterm test + 0.05 * Quizzes + 0.2 * Big practical homework + 0.3 * Final test + 0.15 * Home assignments
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