1. SylabUZ
2. Wydział Nauk Ścisłych i Przyrodniczych
3. semestr zimowy 2022/2023
4. Computer science and econometrics - drugiego stopnia z tyt. magistra
5. Forecasting and Simulation

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Forecasting and Simulation - opis przedmiotu

Cel przedmiotu

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

Wymagania wstępne

Zapisz zmiany

Knowledge of probability calculus, mathematical statistics, econometrics, and basic programming.

Zakres tematyczny

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)

 

 

 

 

Metody kształcenia

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.

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

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.

Literatura podstawowa

1. Peter J. Brockwell , Richard A. Davis, Introduction to Time Series and Forecasting, Springer Link, 2016.
2. R.J. Hyndman, G. Athanasopoulos, Forecasting: principles and practice 2nd Edition, OTexts, 2018.
3. A. Nielsen, Practical Time Series Analysis: Prediction with Statistics and Machine Learning, O'Reilly Media, 2019.
4. W. McKinney, Python for Data Analysis, O'Reilly Media, 2022.
5. C. Ismay, A.Y. Kim, Statistical Inference via Data Science: A ModernDive into R and the Tidyverse, Taylor & Francis Ltd, 2020.

Literatura uzupełniająca

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.

Uwagi

Zmodyfikowane przez dr Jacek Bojarski, prof. UZ (ostatnia modyfikacja: 07-02-2024 19:51)