Mathematical Methods for Economics and Finance

The course is intended to time series models with economic and financial applications. The main aim is to learn about models and their characteristics, criteria of the model selection and to discuss possible aplications in the microeconomics, macroeconomics and in finance. Students will posses the skills of modelling economic phenomana using time series models.

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)

d. Autoregression distributed lags model.

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.

Remote lectures and classes will be done in a synchronous way.

Remote lectures and classes:

1. MS Teams' course code: xqocswg (you should be logged in by MS Office 365 account provided by Nicolaus Copernicus University)

2. The course will take place on-line via MS Teams following a schedule given on the course's USOS site.

3. Moodle site: https://moodle.umk.pl/WNEiZ/course/view.php?id=743

with the course materials, additional information, forum, scripts, data etc.

(password will be given during first classes)

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

Communication channels with the lecturer:

a) On-line office hours,

b) E-mail,

c) Moodle's forum,

d) MS Teams chat.

Enders W., Applied Econometrics Time Series, 2015, 4th edition, Wiley & Sons

Lütkepohl H., New Introduction to Multiple Time Series Analysis, 2005, Springer‐Verlag, Berlin

Greene W.H., Econometric analysis, Pearson; 8th edition (March 30, 2017)

Tsay R. Analysis of financial time series, Wiley; 3rd edition (August 30, 2010)

W1. Written exam +++

U1. Individual project +++

U1. Lab observations +++

U2. Individual project ++

U2. Lab observations +++

U3. Individual project ++

K1. Lab observations ++

K2. 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%)