| Course title | State Final Exam: Analysis of Economic Data and Decision Making |
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| Course code | KMI/BZAE |
| Organizational form of instruction | no contact |
| Level of course | unspecified |
| Year of study | not specified |
| Semester | Winter and summer |
| Number of ECTS credits | 0 |
| Language of instruction | Czech |
| Status of course | unspecified |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Lecturer(s) |
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| Course content |
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Topic Statistic Modeling and Analysis of Temporal Series: 1. Regression analysis - basic principles. The problem of correct regression model selection. 2. Simple linear and non-linear regression. Multiple regression. Estimation of regression parameters. 3. Interpretation of regression coefficients. Interpolation and prediction based on regressions models. 4. Overall relevancy of the regression model. Relevancy of individual regression components. Goodness-of-fit measures. 5. Correlation analysis. The shapes of correlation fields. Interpretation of correlation coefficients. 6. Introduction to the time series theory (basic notions, comparability of time series values, summarizing time series values, time series of derived characteristics, moving averages, ?) 7. Basics of time series decompositions. Additive and multiplicative models. 8. Classical methods of trend modeling, parameter estimation of specific types of trend models. 9. Exponential smoothing (fundamentals of simple, double, and Winters exponential smoothing). 10. Seasonality in time series. Regression approach to the seasonality. 11. Hypotheses about existence of constant seasonality. Small trend methods. Models with constant seasonality with linear trend. 12. Cyclic component in time series. Basics of spectral analysis. Identification of significant periods. Model estimation. 13. Residual component of time series. Tests of randomness. 14. White noise. Autocorrelation of residuals. Detection of autocorrelation. 15. Basic principles of Box-Jenkins methodology. Topic Operational Analysis: 1. Mathematical model of linear programming (LP) problem. LP models with binary variables. 2. General properties of solving LP problems. Graphical solution of LP problems with two variables. 3. Nature of simplex method. Analysis of final simplex tableau and post-optimization analysis. Software to solve LP problems. 4. Basic notions of multiple criteria decision making (MCDM) - dominated, ideal, basal alternatives. Methods of weight selection. 5. MCDM methods of choosing optimal alternative. 6. Data envelopment analysis. Graphical solution of simple problems. LP models for unit efficiency detection. 7. Projects and their representation by network graphs. 8. Calculation of project duration by critical path methods. Identifying slacks in non-critical activities and possibility of their exploitation. Network analysis software. 9. Time-cost and time-resource analysis in deterministic projects. 10. Stochastic projects and their solving by PERT method. Basic probability computations in stochastic projects. C. Topic Econometrics: 1. BASIC PRINCIPLES OF ECONOMETRICS Empirical analysis and econometric modeling. Structure of economic data. Causality in econometric analysis. "Ceteris paribus" principle. 2. CLASSICAL MODEL OF LINEAR REGRESSION Model assumptions, least squares method, parameter estimation, goodness-of-fit measures. Properties of estimates (variance, unbiasedness, bias of estimators in case of violation of model assumptions). Effects of data scaling, functional forms of regression, modeling constant (semi-)elasticity, regression through origin. 3. STATISTICAL INFERENCE AND ECONOMICAL INTERPRETATION OF THE LINEAR REGRESSION MODEL Hypotheses testing in the model: (one and two-sided) t-tests, testing multiple linear restrictions (F-tests and non-nested regression models). Interpretation of results of statistical inference. Asymptotical properties of the model (consistency, asymptotic normality), large sample inference. Residual analysis, forecasting, prediction and confidence intervals. 4. FURTHER ISSUES OF LINEAR REGRESSION Models with qualitative (binary) variables, linear probability model. Model specification errors (exclusion of relevant and inclusion of irrelevant variables) and their effect on the results of analysis. Modifications of classical linear regression model. Models with heteroskedasticity, consequences of heteroskedasticity, weighted least squares method.
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| Learning activities and teaching methods |
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| Learning outcomes |
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The content of this course is the state final examination in statistics, econometric and decision making theory. Students are required to prove their mastery of basic terminology, theoretical principles and calculation operations in these fields and their correct application in particular problems. The first part of the examination covers topics such a probability and random variables. The second part of the examination covers the basic principles of mathematical statistics and inference. The third part of examination covers some classical method of time series analysis. Finally the fourth part consists of some parts from operation analysis.
Students know basic terminology, theoretical principles, and method used in the field of mentioned subjects. Students are able to apply the acquired knowledge in practice. |
| Prerequisites |
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Subjects Teorie pravděpodobnosti a statistika (TPS), Statistické modelování a analýza časových řad (SMAC), Ekonometrie (ENM), Operační analýza (OA)
KMI/COV ----- or ----- KMI/KOA ----- or ----- KMI/OA ----- or ----- KMI/OAA ----- or ----- KMI/OV ----- or ----- KMI/OVA and KMI/CENM ----- or ----- KMI/ENM ----- or ----- KMI/KENM ----- or ----- KMI/YENM and KMI/CSMAC ----- or ----- KMI/KSMAC ----- or ----- KMI/SMAC ----- or ----- KMI/YSMAC |
| Assessment methods and criteria |
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Oral examination
Students have to prove their complex understanding of the basic terminology and principles of aforementioned quantitative methods; they have to be able to respond to particular questions and to apply theoretical knowledge in practical examples. |
| Recommended literature |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester |
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