(in Polish) Mathematical Methods for Economics and Finance 1155-12-E21-0-MMFEF

The lecture and practical classes are in line with the following modules:

1. Univariate time series models for economic and financial applications
a. Stationarity and non-stationarity of the time series (definition and DF/ADF, KPSS test)
b. Deterministic time series models (trend, seasonality)
c. ARIMA models theory and practice (economic and financial applications)
2. Multivariate time series models: VAR model, framework, estimation and testing, Impusle Respone Function, prediciton.
3. Concept of cointegration and error correction model (ECM).
4. Basic characteristics of financial time series and models in finance.

You can already install the essential software (gretl, www: http://gretl.sourceforge.net/) and get familiar with it.

Total student workload

Contact hours: 15h lecture; 30 h laboratory, 30 h consultations Student's own work 75 h. Total student's work 150 h.

Learning outcomes - knowledge

W1: The graduate knows the methods and sources of time series data gathering, specific for economics and finance (K_W06); W2: The graduate knows and understands econometric models necessary for micro and macrolevel studies (K_W06); W3: The graduate knows and interprets financial portfolio construction

Learning outcomes - skills

U1: The graduate interprets the results of econometric and financial models (K_U03); U2: The graduate forecasts future states of micro; macro and financial processes (K_U04); U3: The graduate applies advanced quantitative models and uses statistical software (K_U08)

Teaching methods

Expository teaching methods: - informative and participatory lecture, Exploratory teaching methods: - project work, - practical.

Expository teaching methods

- informative (conventional) lecture
- problem-based lecture
- participatory lecture

Exploratory teaching methods

- practical
- laboratory
- classic problem-solving

Online teaching methods

- cooperation-based methods
- evaluative methods
- exchange and discussion methods
- methods developing reflexive thinking
- methods referring to authentic or fictitious situations

Type of course

compulsory course

Prerequisites

Basic mathematics Statistics and statistical inference

Course coordinators

Marcin Fałdziński
Magdalena Osińska

Assessment criteria

W1. Written exam +++
W2. Written exam++
W3. Written exam++
U1. Individual project +++
U1. Lab observations +++
U2. Individual project ++
U2. Lab observations +++
U3. Individual project ++

Assessment criteria:

60% written exam + 40% practical classes

Grades:

2 (below 60% of points)
3 (60%)
3+ (70%)
4 (80%)
4+ (85%)
5 (90%)

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 1155-12-E21-0-MMFEF in USOSweb