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Financial Mathematics, Second cycle
1 ali 2 year
first or second
slovenian, english
Hours per week – 1. or 2. semester:

There are no prerequisites.

Content (Syllabus outline)

Introduction: the definition and the place of econometrics in the economics, basic methodology of the econometric research.
Linear regression: the method of least squares, The Gauss-Markov Theorem, testing of the general linear assumption, diagnostic methods, important empirical values, residue tests, linearity tests, the Cook Test.
Generalized linear model: heteroskedasticity, autocorrelation of errors, stochastically independent variables, nonlinear regression models, models with instrumental variables. Cointegration, logit and probit models for dichotomous and politomous data.
Panel data: modelling and definitions, parameter estimation, hypothesis testing, multidegree panel data, discrete choice models.
Simultaneous systems regression equations: various forms of the systems, identification equation of the system, various estimation methods for the simultenous system of equations.
Vector autoregression, model verification. Cointegrated vector autocorrelation.

  1. W. H. Greene: Econometric analysis, 6th ed., Upper Saddle River (N.J.) : Pearson/Prentice Hall, cop. 2008.
  2. D. N. Gujarati, D. C. Porter: Basic econometrics, 5th ed., Boston [etc.] : McGraw-Hill, cop. 2009.
  3. M. Verbeek: A guide to modern econometrics, 3rd ed., Chichester : J. Wiley & Sons, cop. 2008.
  4. S. Weisberg: Applied linear regression, 3rd ed., Hoboken (New Jersey) : J. Wiley & Sons, cop. 2005.
  5. J. M. Wooldridge: Introductory econometrics : a modern approach, 3rd ed., Mason, OH : Thomson/South-Western, cop. 2006.
Objectives and competences

Statistical applications in economics naturally lead to econometrics. This gives new, deaper perspective to the statitstics itself on one side, and to the interplay between statistics and economics on the other side. The course is a necessary prerequisite for anybody who will use statistics for the analysis of the processes in the economics.
Since the content is of great practical importance we expect that also specialists from financial practice will present their work experience during the course.

Intended learning outcomes

Knowledge and understanding:
Understanding of statistical applications to economics, interplay between statistical reasoning and economics.
Statistics is the language of the quantitative economics. On one side, application is immediate, on the other side the knowledge will satisfy to persue doctoral studies in economics.
The interplay between application, statistical modelling, economics feedback information, and application stimulation for mathematical reasoning.
Transferable skills:
The skills obtained are transferable to other areas of mathematical modelling, but the gist of the course is its immediate applicability.

Learning and teaching methods

Lectures, exercises, homeworks, consultations


Homework written exam
Oral exam
grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

Lecturer's references

Mihael Perman:
BLEJEC, Matjaž, LOVREČIČ SARAŽIN, Marko, PERMAN, Mihael, ŠTRAUS, Mojca. Statistika. Piran: Gea College, Visoka šola za podjetništvo, 2003. X, 150 str., graf. prikazi, tabele. ISBN 961-6347-43-8. [COBISS-SI-ID 122243328]
PERMAN, Mihael. Order statistics for jumps of normalised subordinators. Stochastic Processes and their Applications, ISSN 0304-4149. [Print ed.], 1993, vol. 46, no. 2, str. 267-281. [COBISS-SI-ID 12236633]
HUZAK, Miljenko, PERMAN, Mihael, ŠIKIĆ, Hrvoje, VONDRAČEK, Zoran. Ruin probabilities and decompositions for general perturbed risk processes. Annals of applied probability, ISSN 1050-5164, 2004, vol. 14, no. 3, str. 1378-1397. [COBISS-SI-ID 13168985]