# Syllabus - Zagrebačka škola ekonomije i managementa

```Zagrebačka škola ekonomije i managementa
Syllabus: Econometrics
SYLLABUS
Econometrics
(ECTS 8)
LECTURERS
Lecturers
Ivo Krznar
Davor Kunovac
Contact
E-mail:
ivo.krznar@hnb.hr
davor.kunovac@hnb.hr
COURSE DESCRIPTION
The Econometric course introduces students to classical theory of econometrics. Student will
become familiar with classical regression theory, estimation using ordinary least square methods as
well as theory behind maximum likelihood methods and generalized method of moments. Different
methods of hypothesis testing will be introduced, specific for each of the stated estimation methods.
Apart from elaboration of theory of each of the mentioned concepts, a computer practicum will be
an integral part of this course where students will familiarize themselves with applications of
theoretical constructions.
HOURS
Theoretical classes
PC lab
Total
30 hours (15 weeks * 2 hours)
30 hours (15 weeks * 2 hours)
60 hours
PREREQUISITES
Students are also expected to be familiar with statistics and maths course from the prep semester as
well as Eviews 6 (or higher) basics.
Your grade will be determined based on 2 midterms each of which will carry 25% of the total grade
and the exam at the end of classes (with the weight of 50%). Midterms will either require in-depth
literature reviews or problem solving.
One should expect the following percentage performance/grade schedule:
90-100% = excellent (5),
80-89% = very good (4),
1
Syllabus: Econometrics
Zagrebačka škola ekonomije i managementa
70-79% = good (3),
55-69% = pass (2).
PRELIMINARY COURSE OUTLINE
1) Introduction to econometrics, a historical overview of development of econometrics, classical
regression model and OLS method, descriptive properties of OLS estimators
2) Statistical properties of OLS estimators, Gauss-Markov theorem
3) Hypothesis testing I (D-method, least sum of residuals method, ANOVA method)
4) Hypothesis testing II (D-method, least sum of residuals method, ANOVA method)
5) Asymptotic theory I , modes of convergence, central limit theorem
6) Asymptotic theory II , modes of convergence, central limit theorem
7) Maximum likelihood theory (ML), ML estimator and its properties, information matrix,
Cramer-Rao theorem
8) Asymptotic properties of ML estimators
9) ML tests (Likelihood ratio, Wald test)
10) Qualitative variables (logit, probit, tobit)
11) QML and generalized linear model
12) Heteroskedasticity , White estimator, White test, Soldfeld-Quandt test, Gleisjer test,
13) Autocorellation, Newey-West estimator, FGLS, Durbin-Watson test, Godfrey test, Box-Pierce
test
14) Exogenity and simultaneity I (simultaneous equations and exogenity)
15) Exogenity and simultaneity II (instrumental variables, Hausman test)