SylabUZ
| Course name | Forecasting and Simulation |
| Course ID | 11.0-WK-CSEED-FS-S22 |
| Faculty | Faculty of Exact and Natural Sciences |
| Field of study | computer science and econometrics |
| Education profile | academic |
| Level of studies | Second-cycle studies leading to MS degree |
| Beginning semester | winter term 2022/2023 |
| Semester | 1 |
| ECTS credits to win | 7 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 15 | 1 | - | - | Exam |
| Laboratory | 30 | 2 | - | - | Credit with grade |
The objective of the course is to acquaint students with practical methods of forecasting and computer simulations of random phenomena based on econometric models.
Knowledge of probability calculus, mathematical statistics, econometrics, and basic programming.
Lecture:
1. Discussion of the scope of materials in mathematical statistics and econometrics required for the course. (2 hours)
2. Deterministic and stochastic simulation. The Monte Carlo method. (4 hours)
3. Random number generators. Simulation accuracy. (1 hour)
4. Econometric forecasting. Forecast error. (2 hours)
5. Simple forecasting methods. Determining dynamic indices. (2 hours)
6. Time series filtering. Exponential smoothing of time series. (2 hours)
7. Future inference based on econometric models. (2 hours)
a. Forecasting based on linear models. (2 hours)
b. Forecasting based on nonlinear models. (2 hours)
Laboratory:
1. Discussion of a selected high-level programming language along with its libraries. Introduction to programming techniques. (2 hours)
2. Methods of data entry and recording. Reporting techniques, graphical data presentation. (4 hours)
3. Simulation of selected random phenomena. Presentation of results. (6 hours)
4. Simple forecasting, dynamic indices. Evaluation of distribution and forecast error parameters. Presentation of results. (2 hours)
5. Smoothing time series, forecasting. Evaluation of distribution and forecast error parameters. Presentation of results. (6 hours)
6. Forecasting based on linear models. Forecast error. Presentation of results. (5 hours)
7. Forecasting based on nonlinear models. Forecast error. Presentation of results. (5 hours)
Lecture – traditional format. Laboratory – At the beginning of the classes, the instructor acquaints students with the practical analysis methods discussed during the lecture. Then, a topic is assigned for development to reinforce the material. After the classes, additional topics are assigned for development.
| Outcome description | Outcome symbols | Methods of verification | The class form |
1. Each student undertakes a project to assess if they have achieved the learning outcomes at a minimum level.
2. Written exam on forecasting issues and simulation methods.
The final grade for the course consists of the laboratory grade (60%) and the exam grade (40%). A condition for passing the course is obtaining a positive grade from both the laboratory and the exam.
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.
Modified by dr Jacek Bojarski, prof. UZ (last modification: 07-02-2024 19:51)
