Эконометрика временных рядов

Бакалавриат 2020/2021

Статус: Курс по выбору (Экономика)

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

Преподаватели: Карамышева Мадина Ринатовна, Малаховская Оксана Анатольевна

Язык: английский

Кредиты: 5

Контактные часы: 64

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

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.

Expected Learning Outcomes

Apply econometric models to empirical models in macroeconomics and finance

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

Quizzes
Home assignments
Big practical homework
big practical homework in the end of the course
Midterm test
Midterm test is not compalsory, it works only for your benet, if you would like to take it
Final test
(if the grade of midterm is higher than the nal grade) and 60% other wise.

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

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

Course Syllabus