SylabUZ
Nazwa przedmiotu | Forecasting and Simulation |
Kod przedmiotu | 11.0-WK-CSEED-FS-S22 |
Wydział | Wydział Matematyki, Informatyki i Ekonometrii |
Kierunek | Computer science and econometrics |
Profil | ogólnoakademicki |
Rodzaj studiów | drugiego stopnia z tyt. magistra |
Semestr rozpoczęcia | semestr zimowy 2022/2023 |
Semestr | 1 |
Liczba punktów ECTS do zdobycia | 7 |
Typ przedmiotu | obowiązkowy |
Język nauczania | angielski |
Sylabus opracował |
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Forma zajęć | Liczba godzin w semestrze (stacjonarne) | Liczba godzin w tygodniu (stacjonarne) | Liczba godzin w semestrze (niestacjonarne) | Liczba godzin w tygodniu (niestacjonarne) | Forma zaliczenia |
Wykład | 15 | 1 | - | - | Egzamin |
Laboratorium | 30 | 2 | - | - | Zaliczenie na ocenę |
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.
Opis efektu | Symbole efektów | Metody weryfikacji | Forma zajęć |
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.
Zmodyfikowane przez dr Jacek Bojarski, prof. UZ (ostatnia modyfikacja: 07-02-2024 19:51)