1. SylabUZ
2. Faculty of Exact and Natural Sciences
3. winter term 2020/2021
4. computer science and econometrics - Second-cycle studies leading to MS degree
5. Forecasting and Simulation

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Forecasting and Simulation - course description

Aim of the course

The aim of the course is to acquaint students with practical methods of forecasting and computer simulation of random phenomena based on econometric models.

 

Prerequisites

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Knowledge of the theory of probability, mathematical statistics, econometrics and the basics of programming.

 

Scope

Lecture
1. Discussion of the material scope of mathematical statistics and econometrics required for the course. (2 hours.)
2. Deterministic, stochastic simulation. Monte Carlo method. (4 hours)
3. Random number generators. The accuracy of the simulation. (1 hour)
4. Econometric forecast. Forecast error. (2 hours.)
5. Simple forecasting methods. Determination of dynamics indexes. (2 hours.)
6. Filtering of time series. Exponential smoothing of time series. (2 hours.)
7. Reasoning into the future on the basis of econometric models. (2 hours.)
          a. Forecasting based on linear models. (2 hours.)
          b. Forecasting based on nonlinear models. (2 hours.)

Laboratory
1. Overview of the R-project program and selected statistical packages. Introduction to programming techniques in R-project. (2 hours.)
2. Methods of entering and saving data. Analysis reporting techniques, graphical data presentation. (4 hours)
3. Simulation of selected random phenomena. Presentation of the results. (6 hours)
4. Simple forecast, Dynamics indices. Assessment of the distribution and parameters of the forecast error. Presentation of the results. (2 hours.)
5. Smoothing time series, forecast. Assessment of the distribution and parameters of the forecast error. Presentation of the results. (6 hours)
6. Forecast based on linear models. Forecast error. Presentation of the results. (5 hours)
7. Forecast based on nonlinear models. Forecast error. Presentation of the results. (5 hours)

 

 

Teaching methods

Lecture - traditional.
Laboratory - At the beginning of the classes, the lecturer introduces students to the practical methods of analysis discussed in the lecture. Then students receive a task to be solved. Finally, the solutions are discussed.

 

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

1. Each student carries out a project that allows to assess whether he has achieved the learning outcomes to a minimum degree.
2. Written exam on forecasting and simulation methods.

The grade for the subject consists of the laboratory grade (60%) and the exam grade (40%). The condition for passing the course is a positive assessment from the laboratory and the exam.

Recommended reading

1. G.E.P Box, G.M. Jenkins, Analiza szeregów czasowych, PWN, Warszawa, 1983.
2. J. Lesków, Prognozowanie i symulacje, Wydawnictwo uczelniane, Nowy Sacz, 2002.
3. A. Luszniewicz, T. Słaby, Statystyka stosowana, PWE, Warszawa, 1996.
4. Prognozowanie i symulacja, pod redakcja W. Milo, Wydawnictwo Uniwersytetu Łódzkiego, Łódz, 2002.
5. Z. Pawłowski, Prognozy ekonometryczne, PWN, Warszawa, 1973.
6. W. Welfe, A. Welfe, Ekonometria stosowana, PWE, Warszawa, 2003.
7. A. Zelias, Teoria prognozy, PWE, Warszawa, 1984. 8. A. Zelias, B. Pawełek, S. Wanat, Prognozowanie ekonometryczne, teoria, przykłady, zadania, WN PWN, Warszawa, 2003.

Further reading

1. J. Koronacki, J. Mielniczuk, Statystyka dla studentów kierunków technicznych i przyrodniczych, WNT, Warszawa, 2004.
2. M. Gruszynski, Modele i prognozy zmiennych jakosciowych w finansach i bankowosci, Wydawnictwo uczelniane SGH, Warszawa, 2002.
3. W. Tarnowski, Symulacja komputerowa procesów ciagłych, Wydawnictwo uczelniane WSI, Koszalin, 1995.
4. A. Welfe, Ekonometria, PWE, Warszawa, 2005.

 

Notes

Modified by dr Jacek Bojarski, prof. UZ (last modification: 18-11-2020 21:07)