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Course Syllabus

Bachelor 2021/2022

Econometrics II

Type: Compulsory course (International Business and Management Studies)
Area of studies: Management
Delivered by: Department of Management
Where: St.Petersburg School of Economics and Management
When: 3 year, 3, 4 module
Mode of studies: offline
Open to: students of one campus
Instructors: Yuliya Averyanova, Arsen Davitadze, Dmitry Schapov, Elena Taraskina, Evgeny Zazdravnykh
Language: English
ECTS credits: 6
Contact hours: 60

Course Syllabus

Full Syllabus

Abstract

This course provides students with skills in basic econometrics analysis for management studies. In addition, the course covers the theoretical aspect of linear and discrete choice models. These models are the most popular ones in econometrics analysis for management studies, and they are frequently used for empirical term papers and bachelor theses. In sum, the course provides a balanced study of applied and theoretical aspects of econometrics, all of which are necessary for basic econometric analysis.
Learning Objectives

Learning Objectives

  • Students master their skills in the linear regression analysis.
  • Students learn how to estimate the model with the binary dependent variable.
  • Students learn how to estimate FE and RE panel models
  • Students learn how to estimate Diff-in-Diff model
  • Students learn how to estimate simple time series models
Expected Learning Outcomes

Expected Learning Outcomes

  • be able to apply DiD model
  • be able to estimate basic time series regression model
  • be able to estimate GARCH and ARCH models
  • be able to estimate the IV regression model
  • be able to estimate time series models measuring basic causal effects
  • be able to identify cases when it is possible to use IV regression models
  • be able to interpret ARCH and GARCH models
  • be able to interpret the DiD model
  • be able to interpret the model
  • be able to plot time series data in STATA
  • the understanding of what a strong and a weak instrument is
  • the understanding of what a time series econometric model is
  • the understanding of what an experiment in econometric research is
Course Contents

Course Contents

  • Instrumental Variables Regression
  • Experiments and Quasi-Experiments
  • Introduction to Time Series Regression and Forecasting
  • Estimation of Dynamic Causal Effects
  • Additional Topics in Time Series Regression
Assessment Elements

Assessment Elements

  • non-blocking Seminar Activity
  • non-blocking Exam (Projects)
    The commission and the exam retake: If a student retakes the exam (the first retake), we apply the formula of our course. In the commission, we do not take into account the cumulative mark. In case of dispute on any question, the final decision regarding the matter is taken by a group of lecturers teaching this course.
Interim Assessment

Interim Assessment

  • 2021/2022 4th module
    If the student attends more than 50% of seminars: Final mark=0.30*Mark for the Seminar Activity+0.70*Mark for the Exam (Projects); If the student attends 50% or fewer seminars: Final mark=0.30*Mark for the Seminar Activity*Proportion of attended seminars (over a total number of seminars)+0.70*Mark for the Exam (projects).
Bibliography

Bibliography

Recommended Core Bibliography

  • 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

Recommended Additional Bibliography

  • 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
  • Angrist, J. D. (DE-588)124748430, (DE-576)166629405. (2009). Mostly harmless econometrics : an empiricist’s companion / Joshua D. Angrist and Jörn-Steffen Pischke. Princeton, NJ [u.a.]: Princeton Univ. Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.286816679
  • Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2012). The Econometrics of Financial Markets. New Jersey: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1891894
  • J.D. Angrist, Guido W. Imbens, & D.B. Rubin. (1993). Identification of Causal Effects Using Instrumental Variables. NBER Technical Working Papers. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.p.nbr.nberte.0136
  • 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
  • Verbeek, M. (2017). A Guide to Modern Econometrics (Vol. 5th edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1639496
  • Verbeek, M. (DE-588)170802655, (DE-576)164668535. (2012). A guide to modern econometrics / Marno Verbeek. Chichester: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.357323661
  • 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-627)512715513, (DE-576)298669293, aut. (2013). Introductory econometrics a modern approach Jeffrey M. Wooldridge.