Syllabus

Title
4676 Y1P4 Econometrics
Instructors
Univ.Prof. Dr. Sylvia Frühwirth-Schnatter
Contact details
sylvia.fruehwirth-schnatter@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/01/17 to 02/24/17
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Econometrics
Dates
Day Date Time Room
Monday 05/08/17 12:30 PM - 03:00 PM TC.3.05
Thursday 05/11/17 12:30 PM - 03:00 PM TC.5.13
Monday 05/15/17 09:00 AM - 11:30 AM TC.3.03
Monday 05/22/17 09:00 AM - 11:30 AM TC.5.13
Monday 05/29/17 12:30 PM - 03:00 PM TC.3.05
Thursday 06/01/17 12:30 PM - 03:00 PM TC.5.13
Monday 06/12/17 12:30 PM - 03:00 PM TC.3.05
Monday 06/19/17 09:00 AM - 11:30 AM D5.1.001
Wednesday 06/21/17 09:00 AM - 11:30 AM TC.0.04

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 the student 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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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. For that purpose, practical examples will be presented in class. Data used in these examples are also available to the students and can be downloaded from http://www.wu.ac.at/usr/or/geyer/Basic_Financial_Econometrics.zip. This provides the opportunity for students to replicate the examples on their own, prepare for homework assignments and the final exam.

Preparation: Students are expected to have read the appropriate sections from the lecture notes (see contents below).

Students have to do assignments based on the exercises specified in the lecture notes. The purpose of the assignments is to practice using actual data, to recall the methods' theoretical basis and assumptions, and to get acquainted with empirical evidence on financial data.

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Assessment
  • 50% of the final grade are based on the final exam. An opportunity to retake the final exam may be granted to those who failed the first exam. The first and second take will be aggregated using equal weights to obtain the final grade on the final exam. This will still account for 50% of the overall grade.
  • 40% of the final grade are based on the Homework assignments. Homework assignments can be done in groups consisting of up to 4 students. Each student must be able to explain all aspects of an assignment (i.e. it is not recommended that group members only do parts of an assignment; all group members should work jointly on the assignment, and must take full responsibility).
  • 10% are based on the performance in short tests at the beginning of each class (except the first class). These tests consist of questions related to the topics covered in the previous class.

Students can only pass the class if all three parts are graded positively (i.e.they achieved at least 50% of the maximum total credit points). 



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Prerequisites for participation and waiting lists
Successful completion of Mathematics I and Financial Markets and Instruments
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Readings
1 Author: Alois Geyer
Title: Basic Financial Econometrics

Remarks: Lecture notes
Year: 2014
Content relevant for class examination: Yes
Recommendation: Essential reading for all students
Type: Script
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Recommended previous knowledge and skills

Students 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)
E-mail: sylvia.fruehwirth-schnatter@wu.ac.at
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Other

Section numbers for required readings refer to the lecture notes.

In each unit, one or several practical examples are used to demonstrate principles and methods introduced in class.

Course materials:

Lecture notes

Data files

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Unit details
Unit Date Contents
1

Basics of regression analysis (Sections 1.1 and 1.2) - Part I

Students will be 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. Students will be able to interpret the results of a regression analysis, in particular the coefficients of the equation, goodness of fit indicators, and test statistics.

2 Basics of regression analysis (Sections 1.1 and 1.2) - Part II
3

Specifications (Section 1.6):

Students will be 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, and they will be familiar with considerations and criteria in (the sequence of) selecting regressors.
4

Regression diagnostics and GLS (Sections 1.7 and 1.8):

Students 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. Students will know the consequences of violations of the required properties, and they will know how violations can be accounted for or corrected.
5

Financial time series (Section 2.1):

Students will be familiar with basic definitions and properties of simple and log returns, typical empirical features of financial returns, and frequently used distributions. Students 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):

Students will be 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. Students will know how the estimated coefficients and residuals can be used to determine the appropriateness of a model. They will be able to compute static and dynamic forecasts, and will be able to relate properties of forecasts to autocorrelation patterns.

7

GARCH models (Section 2.5):

Students will be 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. Students will know why and how GARCH models account for stylized facts of financial returns such as non-normality and volatility clustering. Students will know which properties of residuals must be checked, and how variance forecasts can be computed.

8

Non-stationary models (Section 2.3):

Students 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.
9 9.6.2016 Final exam
Last edited: 2016-11-21



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