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Магистратура 2021/2022

Эконометрика. Продвинутый уровень

Статус: Курс обязательный (Финансовая экономика)
Направление: 38.04.01. Экономика
Когда читается: 1-й курс, 1-4 модуль
Формат изучения: без онлайн-курса
Охват аудитории: для своего кампуса
Прогр. обучения: Финансовая экономика
Язык: английский
Кредиты: 8

Course Syllabus

Abstract

The main objectives of the first part of Econometrics are to introduce students to basic econometric techniques and to prepare them to do their own applied work. Students are encouraged to think of the course as a preparation toward their thesis research project. The course is taught in English. The purpose of the course is not only to develop new skills in econometric tools and their application to contemporary economic problems, especially in financial economics, but also to study theoretically econometric methods and to review some sections of econometrics on a solid theoretical background. In the first module of the semester, we cover fundamental topics in time series analysis, such as ARMA models, non-stationary time-series, Brownian motion and unit root tests, cointegration, VAR and VECM. During the second module students study binary choice models (logit, probit, tobit, Heckman) and basic concepts of panel data analysis (pooled regression, fixed and random effects, dynamic panel models, binary choice panel data). All topics are accompanied with real data examples in R, Stata, EViews, and JMulTi. The course is taught in English. Course Pre-requisites: Calculus, Probability Theory and Statistics at an intermediate level. Completion of Mathematics for Economics and Finance course is required. Successful completion of Econometrics will allow students to take the Financial Econometrics class.
Learning Objectives

Learning Objectives

  • During the course students will be introduced to modern approaches in analysing economic and financial data
  • Upon completion of the course students should be:familiar with the basic tools available to economists for testing theories, estimating the parameters of economic relationships in financial markets and forecasting financial and macroeconomic variables;
  • able to read, interpret and replicate the results of published papers in economics and finance using standard computer packages and real-world data
Expected Learning Outcomes

Expected Learning Outcomes

  • - be able to relate simple and partial correlation coefficients via the regression anatomy formula
  • - be able to specify different versions of the diff-in-diff design
  • - demonstrate a basic understanding of the SC estimation technique and inference
  • - differenciate between sharp and fuzzy RD design
  • - explain conditional expectations and their relationship to the population regression function
  • - explain the difference between fixed-effect, random-effects, and first-difference models; the parallel trends assumption
  • - identify assumptions for causal effect estimation in the RD setting
  • Address endogeneity problems
  • Construct linear models for time series data and apply the Box-Jenkins procedure.
  • Derive asymptotic distribution of estimators when the standard regularity conditions do not hold
  • Explain main notions of econometrics
  • Explain specifics of working with time series data
  • Model the dynamics of several variables simultaneously, and analyze structural and reduced-form relations between different time series
  • Test time series for various deviations from stationarity and transform trend- and unit-root stationary processes into stationary ones
  • Use advantage of the panel data, correctly use these models
  • Use discrete choice model, correctly use these models
  • Use models from Optional topics
Course Contents

Course Contents

  • Introduction to Econometrics
  • The Linear Regression Model
  • Instrumental Variables and 2SLS
  • Panel Data Models
  • Other Common Research Designs
  • Review of main characteristics of time series
  • Nonstationary time series. Spurious regressions
  • Unit roots and tests for stationarity. Structural breaks. ARIMA models. Forecasting
  • Volatility modelling
  • Vector autoregressive models
  • Discrete choice models
  • Static and dynamic panel data
  • Optional topics
Assessment Elements

Assessment Elements

  • non-blocking Homework Assignments
  • non-blocking Midterm test (Part I)
  • blocking Written exam (part I)
    Online format.
  • blocking written final exam (Part II)
  • non-blocking Problem sets I
  • non-blocking midterm test (part II)
  • non-blocking Problem sets II
Interim Assessment

Interim Assessment

  • 2021/2022 1st module
  • 2021/2022 2nd module
    0.16 * Homework Assignments + 0.29 * Midterm test (Part I) + 0.55 * Written exam (part I)
  • 2021/2022 3rd module
  • 2021/2022 4th module
    0.5 * 2021/2022 4th module + 0.05 * Problem sets I + 0.05 * Problem sets II + 0.1 * midterm test (part II) + 0.3 * written final exam (Part II)
Bibliography

Bibliography

Recommended Core Bibliography

  • Analysis of financial time series, Tsay, R. S., 2005
  • Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics : An Empiricist’s Companion. Princeton: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=329761
  • Hamilton, J. D. . (DE-588)122825950, (DE-576)271889950. (1994). Time series analysis / James D. Hamilton. Princeton, NJ: Princeton Univ. Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.038453134
  • Verbeek, M. (2004). A Guide to Modern Econometrics (Vol. 2nd ed). Southern Gate, Chichester, West Sussex, England: John Wiley and Sons, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=108185

Recommended Additional Bibliography

  • 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
  • Greene, W. H. (2015). Econometric analysis. Slovenia, Europe: Prentice-Hall International. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.1BF5A5CA
  • 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
  • Morgan, S. L., & Winship, C. (2007). Counterfactuals and Causal Inference : Methods and Principles for Social Research. New York: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=206937
  • Peter Kennedy. (2003). A Guide to Econometrics, 5th Edition. The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.mtp.titles.026261183x
  • Ruud, P. A. (2000). An Introduction to Classical Econometric Theory. Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.oxp.obooks.9780195111644
  • Tsay, R. S. (2002). Analysis of Financial Time Series : Financial Econometrics. New York: John Wiley & Sons, Inc. [US]. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=87319
  • 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
  • Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, Mass: MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=78079