COURSE DESCRIPTION CARD

Transcription

1 COURSE DESCRIPTION CARD Załącznik nr 2 do zarządzenia Rektora nr /12 NOTE: If the course consists of lectures and classes, the Course Description applies to both forms of teaching 1. Course title: Econometric Models and Forecasting 2. Course code in Polish: Modele ekonometryczne i prognozowanie Number of ECTS credits: 6 Course completion method:z/e Course commenced / Year 2014/ Faculty: Finance and Insurance 4. Field of Study: 5. Department of the Field of Study Coordinator: Applied Mathematics 6. Name of tutor: Lectures: Stanislaw Barczak PhD, Tomasz Węgrzyn PhD, Anna Sroczyńska-Baron PhD Classes: Stanislaw Barczak PhD, Tomasz Węgrzyn PhD, Anna Sroczyńska-Baron Eng. PhD Lab classes Examiner: Stanislaw Barczak PhD, Tomasz Węgrzyn PhD, Anna Sroczyńska-Baron PhD 7. Tutor s department: Department of Applied Mathematics 8. Number of contact hours with students: Type of course Full time study Part time study Lectures 28 Classes 30 Foreign language classes Lab classes Seminars Introductory Seminars Other Total hours 58 Examination (hours) 3 9. Course timeframe (no. of semesters) 1 Course commencement / Year 2 Course commencement / Semester 3W 10. Stage of tertiary education: 11. Course status Compulsory for the field of study Compulsory for the specialization X Optional Requirements Compulsory: mathematics, statistics Recommended: - 1 -

2 13. Course objectives: 1) Transfer of knowledge in the field of econometrics theory and practical econometric analysis of economic processes using single-equation linear econometric models with one and multiple explanatory variables 2) Develop skills in problem solving decision making through the use of linear single-equation econometric models with one and multiple explanatory variables 3) Education of social competence related to the development and application of econometric methods 14. Teaching and learning methods: A. Direct student/teacher contact hours: No. Teaching methods Description 1. Lectures using multimedia techniques 2. Exercises using computer hardware and software B. Self-study hours: A multimedia presentation of the theory of econometric methods and their practical use in the processes of economic Use of Software: MS Excel, Gretl. No. Learning methods Description Number of teaching hours Full time study Part time study 28 Total AS:58 AN: 1. The notes analysis Getting familiar with the talks in terms of the presented theory, econometrics and its practical applications 2. Solving tests and tasks Acquisition of proficiency in performing the necessary calculations, the identification of an econometric model, the estimation and interpretation of results 30 Number of hours Full time study Part time study 40 Total BS: 92 BN: 52 Total AS+BS = Total AN+BN =.

3 Examination (E) = 3 Total AS+BS+E= 153 Examination (E) =. Total AN+BN+E =. 15. Key words: econometrics, econometric modeling, econometric modeling of market and business processes, econometric forecasts, dynamic models, time trend models 16. Course content: 1. Operations on matrices, determinant, inverse matrix. 2. Canonical form of a matrix, rank of a matrix, elementary operations on matrices. 3. Econometrics subject. Objectives and methods of econometrics. The economic model and the econometric model: comparisons and ples. 4. Single-equation econometric model. Classification. Stages of econometric research. Forecasting properties. Case studies markets and business applications 5. Methods for selecting the analytical form: a priori analysis, the analysis of the dispersion, the specification of a posteriori. Basic market dependencies. 6. Methods for selection of variables to single-equation linear econometric model: the method of correlation coefficients. 7. The ordinary least squares. The objective function, properties, assumptions with respect to the residuals. 8. Linear econometric model with one explanatory variable: the model assumptions, parameter estimation, the interpretation of the structural parameters of the model. Case studies of model selection. 9. Linear econometric model with multiple explanatory variables: assumptions multivariable regression model, estimation of the model parameters, the issue of independent variables. Case studies: financial markets applications. 10. Verification of single-equation econometric model: measure the structure of the stochastic model, the measurement model fits (model quality), testing the relevance of the structural parameters of the econometric model - the t-test. Identification and testing of the first order autocorrelation of residuals test of Durbin - Watson. 11. The essence of the theory of econometric forecasts - econometric prediction, point prediction, the construction of the forecast interval. Ex ante errors of forecasts: the average and relative prediction error. 12. The linear model of time trend. Identification, estimation, interpretation of structural parameters, the construction of point and interval forecasts. Errors ex ante prediction errors and ex post errors of forecasts. 13. Linear time trend with additive seasonality. Applications and forecasting. 17. Student learning outcome achieved in the course, as related to the outcome intended for the field of study. Methods of outcome achievement evaluation. Student learning outcome intended for the field of study / Symbols FiR1_W06 Student learning outcome achieved in the course Knowledge 1. He knows the rules for the description of economic processes using linear singleequation econometric models of one and several variables. The student is able to characterize and define the basic Methods of assessing student learning outcome achieved in the course The written Documentation 1.List of written material scope of the current - 3 -

4 terminology and classification determination of the scope and methodology of econometric analysis. 2. Written students papers FiR1_W08 FiR1_U02 FiR1_U04 FiR1_K02 FiR1_K05 FiR1_K06 2.Understand the extent to which the introduction of quantitative methods for economic research improves problem solving research with particular emphasis on financial processes Skills 1.Uses skills acquisition and verification of statistical data and the construction of linear single-equation econometric model with one and multiple explanatory variables 2. He uses skills in the field of predictive econometric enabling the construction of short-term forecasts on the basis of singleequation linear econometric model Social skills 1. Capable of public expression, group work and joint problem solving, shows openness to the use of modern analytical methods 2. Participates in working in a team, a financial institution using knowledge of econometrics 3. Can independently update their knowledge and skills and to pass it on to others The written 1.Student participation in the discussion on the possibilities given solution of the problem. 2. Competence in presenting and arguing the proposed solution to the problem. 1.List of written material scope of the current 2. Written students papers 1.Oral form 18. Methods of grading student performance: No. Student performance assessment methods and course completion requirements Description 1. The written Includes all kinds of practical tasks of econometric methods and forecasting of the contents of the program object. Includes questions in the theory of econometrics and its applications in solving econometric tasks. Percentage of the final grade

5 * If students are required to earn credits and pass an, the credit accounts for at least 30% of the final grade 19. Reading list Compulsory reading list: 1. William H. Greene, Econometric Analysis, Prentice Hall, Lutkepohl H., Kratzig M., Applied Time Series Econometrics, Cambridge University Press, Ruey S. Tsay, Analysis of Financial Time Series, A John Wiley & Sons, 2010 Recommended reading: 1. Johston J., Dinardo J., Econometric Methods, McGraw-Hill, Ruey S. Tsay, Analysis of Financial Time Series, Wiley, Language of instruction: English 21. Tutors recommendations: Course is conducted in a computer lab having a package of MS Excel and Gretl