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Regular version of the site
Master 2018/2019

Econometrics I (advanced level)

Type: Compulsory course (Financial Economics)
Area of studies: Economics
When: 1 year, 1 semester
Mode of studies: offline
Instructors: Fabian Slonimczyk, Timur Zekokh
Master’s programme: Financial Economics
Language: English
ECTS credits: 5

Course Syllabus

Abstract

The main objectives of Econometrics I 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 prerequisites of the course are Calculus and Statistics at an intermediate level. The knowledge of some computer-programming is welcome. The course is taught in English.
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

  • Explain main notions of econometrics
  • Explain conditional expectations and their relationship to the population regression function
  • Be able to relate simple and partial correlation coefficients via the regression anatomy formula
  • Calculate confidence interval
  • Derive robust standard errors
  • Apply econometric techniques to real economic situations
  • Test for heteroskedasticity
  • Address endogeneity problems
  • Outline the conditions under which nonlinear estimators are consistently estimated
Course Contents

Course Contents

  • Introduction to Econometrics
    The FAQS of economics research. Causal Relationships. Experiments and Quasi-experiments. Identification and Statistical Inference. The Selection Problem. Cross Section and Longitudinal Data
  • The Simple Regression Model.
    Derivation of OLS estimates. Mechanics and Properties. Units of measurement and functional form. Unbiasedness and efficiency
  • Multi-variate Regression Analysis
    Motivation: multiple sources of variation. Mechanics and interpretation of OLS. The “partialling out" interpretation and linear projections. Unbiasedness and efficiency: the Gauss-Markov Theorem
  • Inference in the Multi-variate Regression Model
    Sampling distributions of the OLS estimators. Testing Hypothesis. Confidence Intervals.
  • Asymptotic Properties of OLS
    Consistency, asymptotic normality and asymptotic efficiency. The LM test. Sources of endogeneity: omitted variables, measurement error, simultaneity
  • Further Issues in OLS estimation
    Data scaling and beta scores. Quadratic and interaction terms. Prediction. Dummy Variables. Proxy variables. Missing data and outliers.
  • Heteroscedasticity
    Consequences for OLS. Heteroscedasticity-robust inference. BreuschPagan and White tests. WLS and FGLS.
  • Instrumental Variables and 2SLS
    Instruments as a solution to endogeneity. Reduced form equations. Exclusion restrictions. Rank condition. Two-stage least squares and GMM.
  • Maximum Likelihood
    ML Estimators. Likelihood ratio, Wald and LM tests. GLS and 2SLS as ML estimators
Assessment Elements

Assessment Elements

  • non-blocking Homework Assignments
  • non-blocking Midterm test
  • blocking Written final exam
Interim Assessment

Interim Assessment

  • Interim assessment (1 semester)
    0.16 * Homework Assignments + 0.29 * Midterm test + 0.55 * Written final exam
Bibliography

Bibliography

Recommended Core Bibliography

  • 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
  • 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

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
  • 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
  • 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
  • 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