Syllabus

Title
4355 Econometrics
Instructors
Assoz.Prof. PD Dr. Zehra Eksi-Altay, BSc.MSc.
Contact details
zehra.eksi@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/01/24 to 02/18/24
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Econometrics
Dates
Day Date Time Room
Monday 05/13/24 01:30 PM - 04:00 PM TC.1.01 OeNB
Thursday 05/16/24 10:00 AM - 12:30 PM TC.1.01 OeNB
Thursday 05/23/24 10:00 AM - 01:00 PM TC.1.02
Monday 05/27/24 01:30 PM - 04:30 PM TC.1.01 OeNB
Monday 06/03/24 01:30 PM - 04:00 PM TC.2.02
Thursday 06/06/24 10:00 AM - 12:30 PM TC.1.01 OeNB
Monday 06/10/24 01:30 PM - 04:00 PM TC.1.01 OeNB
Thursday 06/13/24 10:00 AM - 12:30 PM TC.1.01 OeNB
Thursday 06/20/24 10:00 AM - 12:00 PM TC.0.02

On this page:

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Contents
This course covers basic topics in econometrics with a focus on applications in finance.
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Learning outcomes

After completing this course participants will

  • have the ability to apply and interpret the results of regression analyses
  • be familiar with key aspects relevant for the specification of a regression model
  • understand the relevance and implications of various assumptions in each step of the analysis
  • know why and how specific properties of regression residuals must be tested
  • understand the consequences of violations of certain assumptions, and know how to account for them
  • be familiar with basic definitions of financial returns, and able to derive and interpret their empirical (dynamic) properties
  • know how to distinguish non-stationary from stationary series and how to apply unit-root tests
  • understand the purpose and the basic principles of GARCH models, and how to estimate and test such models
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Attendance requirements

Participants are required to attend each class, except for serious illness and/or important private concerns. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

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Teaching/learning method(s)

The course is taught using a combination of lectures and practical examples demonstrated in class. The lectures are aimed at establishing a sound understanding of the main ideas and basic principles of econometric methods and analyses. Special emphasis is put on applications using financial data.

     

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    Assessment
    • 30% of the final grade is based on the midterm exam.
    • 40% of the final grade is based on the final exam.
    • 30% of the final grade is based on homework assignments. Homework assignments can be done in groups consisting of up to 3 members. Each member of a group must be able to explain all aspects of an assignment (i.e. group members must not only do parts of an assignment (i.e. must not share the workload); all group members should work jointly on the assignment and must take full responsibility).

    Grading scheme:

    • 1 if final score >= 0.875
    • 2 if final score < 0.875 and >=0.75
    • 3 if final score < 0.75 and >=0.625
    • 4 if final score < 0.625 and >=0.5
    • 5 if final score < 0.5

     

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    Readings

    Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

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    Recommended previous knowledge and skills

    Participants should be familiar with the following topics on an undergraduate level:

    • Mathematics (e.g. matrix algebra, polynomials, derivatives, etc.)
    • Probability(e.g., distributions, conditional probability, expectation operators, etc.).
    • Statistics(e.g., descriptive statistics, sampling distributions, hypothesis testing,etc.)
    • Computing: Excel, EViews, R
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    Availability of lecturer(s)

    I answer e-mails as soon as possible (zehra.eksi@wu.ac.at )

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    Comments on coursera videos

    Comments on videos of the coursera course "Econometrics Methods and Applications" are available at https://www.wu.ac.at/fileadmin/wu/d/i/ifr/comments.Econometrics_Methods_and_Applications.by.Alois.Geyer.pdf.

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    Unit details
    Unit Date Contents
    1

    Basics of regression analysis (sections 1.1 and 1.2)

    Participants will become familiar with fundamental aspects of linear regression analysis. In particular, they will be able to use the appropriate terminology, will understand the principle of least squares, and will have a clear understanding about the relevance and implications of various assumptions in each step of the analysis. Participants will be able to interpret the results of a regression analysis, in particular, the coefficients of the equation, the goodness of fit indicators, and test statistics.
    2

    Specifications (section 1.6):

    Participants will become familiar with key aspects of the specification of a regression model. In particular, they will know how and when to apply log transforms, dummy variables, and interaction terms. They will also know how to interpret the coefficients in each of these cases. In addition, they will know which consequences are associated with omitted and irrelevant regressors, they will become familiar with considerations and criteria in (the sequence of) selecting regressors, and they will learn about the role of cross-validation in the specification search.
    3

    Regression diagnostics and GLS (sections 1.7 and 1.8):

    Participants will know which properties of regression residuals must be tested. In particular, they will know why non-normality, heteroscedasticity, and autocorrelation must be tested, how these features can be tested, and how these properties are related to the assumptions introduced in unit 1. Participants will know the consequences of violations of the required properties, and they will know how violations can be accounted for or corrected.
    4

    Endogeneity (Section 1.9)

    Participants will know how violations of the exogeneity assumption (i.e. various types of endogeneity) can be accounted for or corrected using instrumental variables estimation.
    5

    Financial time series (section 2.1):

    Participants will become familiar with basic definitions and properties of simple and log returns, typical empirical features of financial returns, and frequently used distributions. Participants will also know how to use and interpret autocorrelation analyses to describe the dynamic properties of (absolute) returns. They will be able to define abnormal returns, and understand the basic idea and results of event studies.
    6

    ARMA models (section 2.2):

    Participants will become familiar with definitions and properties of autoregressive (AR), moving average (MA) and ARMA models. They will know how to identify which of these models is appropriate on the basis of (partial) autocorrelations. Participants will know how the estimated coefficients and residuals can be used to determine the appropriateness of a model. They will be able to compute forecasts and will be able to relate properties of forecasts to autocorrelation patterns.
    7

    Non-stationary models (section 2.3):

    Participants will know how to distinguish non-stationary from stationary series in terms of statistical features and forecasting behavior. They will understand the basic idea and special nature of unit-root tests, how the tests are carried out, and which conclusions about the underlying series can be derived.
    8

    GARCH models (section 2.5):

    Participants will become familiar with the purpose and the basic principles of GARCH models. They will know how to specify the model equations (conditional mean and variance), and know how to estimate GARCH models using the maximum likelihood principle. Participants will know why and how GARCH models account for stylized facts of financial returns such as non-normality and volatility clustering. Participants will know which properties of residuals must be checked, and how variance forecasts can be computed.

    Last edited: 2024-02-12



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