Course syllabus EKM1A - Econometrics I (FBE - SS 2019/2020)


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Course code: EKM1A
Course title in language of instruction: Econometrics I
Course title in Czech: Econometrics I
Course title in English: Econometrics I
Mode of completion and number of credits: Exam (5 credits)
(1 ECTS credit = 28 hours of workload)
Mode of delivery/Timetabled classes: full-time, 1/2 (hours of lectures per week / hours of seminars per week)
Language of instruction: English
Level of course: bachelor
Semester: SS 2019/2020
Name of lecturer: doc. Ing. Václav Adamec, Ph.D. (examiner, instructor, lecturer, supervisor)
Prerequisites: Statistics or Statistics
 
Aims of the course:
Obtaining theoretical knowledge and practical experience with construction of basic econometric models based on linear regression and models of univariate time series. Students are able to evaluate the econometric models, interpret in economic context and apply for predictions. Students can apply statistical software. Knowledge and skills learned in this course are expected to be used during work on student bachelor's thesis.
 
Course contents:
1.Introduction to Econometry (allowance 1/0)
 
a.Definition of Econometry, evolution and history.
b.Basic steps of econometric analysis
c.Econometric data types

2.Regression and correlation analysis, error term. (allowance 4/6)
 
a.Regression analysis
b.Ordinary Least Squares method (OLS)
c.Error term, residuals
d.Analysis of variance (ANOVA)
e.Correlation analysis

3.Statistical hypothesis testing, confidence intervals. (allowance 2/4)
 
a.Tests of significance for regression coefficients (t-tests) and test of overall model significance (F-test)
b.Tests of model specification, non-linearity test and RESET test
c.Confidence interval for regression coefficients
d.Confidence interval and prediction interval for the model
e.Testing coefficient of correlation

4.Gauss-Markov theorem, classical model assumptions (allowance 2/4)
 
a.Classical assumptions
b.Properties of OLS estimator

5.Introduction to time series analysis (allowance 1/2)
 
a.Time series data, definition, properties, types
b.Time series dynamics

6.Models of time series (allowance 4/8)
 
a.Qualitative (expert) methods
b.Moving averages, exponential filters
c.Time series decomposition
d.Modeling seasonality
e.Models based on filters
f.Causal regression models
g.Criteria of model fit

7.Applied econometry (allowance 0/4)
 
a.Student presentations, discussion

 
Learning outcomes and competences:
Generic competences:
 
-Ability to apply knowledge
-Basic computing skills
-Science and research skills
-Skilled at utilizing and processing information
-Work in team

Specific competences:
 
-Ability to apply the principles of constructing model of univariate time series
-Constructing a statistical model for real econometric data
-Understanding the methods to describe relationship between two economic variables
-Understanding the principles of constructing econometric model

Type of course unit: required
Year of study: Not applicable - the subject could be chosen at anytime during the course of the programme.
Work placement: There is no compulsory work placement in the course unit.
Recommended study modules: -
 
Learning activities and study load (hours of study load):
Type of teaching methodDaily attendance
Direct teaching
     lecture14 h
     practice28 h
Self-study
     preparation for exam58 h
     preparation for regular testing15 h
     preparation of presentation5 h
     writing of seminar paper20 h
Total140 h
 
Assessment methods:
A credit is granted on the basis of group project (>= 50% score), midterm exam (score at least 50%) and active participation in labs (at most 2 missed labs). Active lab participation is further assessed by 1 point per lab. Credit is required for admission to the final exam. Passing final exam requires at least 50% score. Course grade is made on the basis of the final exam, midterm exam, project and active participation: A [91 – 100]; B [82 – 91); C [73 – 82); D [64 – 73); E [55 – 64); F [0 – 55). The examiner may adjust the grade by 1 step in both directions. The course cannot be taken during overseas internship.
 
Recommended reading:
TypeAuthorTitlePublished inPublisherYearISBN
RQGUJARATI, D N. -- PORTER, D C.Basic econometricsBostonMcGraw-Hill Irwin978-007-127625-2
RQASHENFELTER, O. -- LEVINE, P B. -- ZIMMERMAN, D J.Statistics and econometrics : methods and applicationsNew YorkJohn Wiley & Sons20030-471-10787-5
RQWOOLDRIDGE, J M.Introductory econometrics: a modern approachMason, OhioSouth-Western2008978-0-324-66054-8
REKMENTA, J.Elements of econometricsAnn ArborUniversity of Michigan Press20110-472-10886-7
REUsing econometrics: a practical guideBostonAddison Wesley Pearson0-321-31649-5

RQrequired
RErecommended


Last modification made by Ing. Jiří Gruber on 12/04/2019.

Type of output: