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Бакалавриат 2019/2020

Анализ временных рядов и панельных данных

Направление: 38.03.01. Экономика
Когда читается: 4-й курс, 1, 2 модуль
Формат изучения: Full time
Язык: английский
Кредиты: 4

Программа дисциплины


Time Series and Panel Data Analysis (intermediate level) is a two-module course designed for fourth year ICEF students. The course is divided into two parts. The first part covers time series theory and methods, while the second part goes over panel data analysis. Students will learn basic theoretical results and how to estimate time series and panel data in practice with the help of computational software. The course is taught in English. Course Pre-requisites: Statistics, Mathematics for Economists, Introduction to Econometrics, Introduction to Economics.
Цель освоения дисциплины

Цель освоения дисциплины

  • introduce the students to the modern methods of time series and panel data analysis
  • prepare students for individual work, in particular on their bachelor's theses
Результаты освоения дисциплины

Результаты освоения дисциплины

  • Explain specifics of time series data
  • construct linear models for time series data and apply the Box-Jenkins procedure
  • test data for stationarity and transform non-stationary series into stationary ones.
  • model the dynamics of several variables simultaneously, and analyze relations between different time series
  • be able to estimate different time series models with the help of statistical software
  • construct forecasts for macroeconomic and financial variables
  • model dependence in conditional variance of times series data
  • estimate the basic models of conditional heteroskedacticity using statistical software
  • explain specifics of panel data: when it is used and what flexibility it adds to econometric models
  • construct and estimate linear models with unobserved heterogeneous effects
  • compute the pooled OLS, fixed effects, and random effects estimators
  • compute Arellano-Bond estimator
  • construct nonlinear models for panel data, in particular, binary choice models, and estimate those models in practice
Содержание учебной дисциплины

Содержание учебной дисциплины

  • Time series: basic concepts
    Definition of time series. Introduction of main characteristics of time series (stationarity, ergodicity, autocovariance function, correlogram). Lag operator.
  • ARMA models
    Autoregressive models. Moving-Average models. Wold decomposition. Moments, stationarity and invertibility conditions. Autoregressive Moving-Average models. Aggregation. ADL models.
  • Nonstationary time series
    Deviations from stationarity: unit roots, deterministic trends, structural breaks. Tests of stationarity
  • Multivariate time series
    VAR models: properties and characteristics. Granger causality.
  • Estimation and forecasting
    Estimation of ARMA and VAR models. Forecasting. Properties of forecasts. HAC variance estimation
  • Conditional heteroskedasticity
    ARCH and GARCH models: introduction, properties, estimation
  • Panel data: Introduction
    Introduction to panel data analysis. Advantages of panel data
  • Linear Panel Data Models
    Fixed effects and random effects. Between, within, and pooled estimators. Estimation and hypothesis testing
  • Dynamic Panel Data Models
    Dynamic panels. Arellano-Bond estimator.
  • Nonlinear panel models
    Binary response models with panel data. Logit and probit models of panel data.
Элементы контроля

Элементы контроля

  • неблокирующий Created with Sketch. problem sets
  • неблокирующий Created with Sketch. essay
  • неблокирующий Created with Sketch. midterm
  • неблокирующий Created with Sketch. final exam
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (2 модуль)
    0.15 * essay + 0.5 * final exam + 0.2 * midterm + 0.15 * problem sets
Список литературы

Список литературы

Рекомендуемая основная литература

  • A. Colin Cameron, & Pravin K. Trivedi. (2010). Microeconometrics Using Stata, Revised Edition. StataCorp LP. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.tsj.spbook.musr
  • Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics : Methods and Applications. New York, NY: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=138992
  • 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
  • Stock, J. H., & Watson, M. W. (2015). Introduction to Econometrics, Update, Global Edition (Vol. Updated third edition). Boston: Pearson Education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1419285
  • Tsay, R. S. (2010). Analysis of Financial Time Series (Vol. 3rd ed). Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=334288
  • Wooldridge, J. M. . (DE-588)131680463, (DE-576)298669293. (2006). Introductory econometrics : a modern approach / Jeffrey M. Wooldridge. Mason, Ohio [u.a.]: Thomson/South-Western. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.250894459
  • Wooldridge, J. M. . (DE-588)131680463, (DE-576)298669293. (2010). Econometric analysis of cross section and panel data / Jeffrey M. Wooldridge. Cambridge, Mass. [u.a.]: MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.263114414

Рекомендуемая дополнительная литература

  • Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Berlin: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=145686