Course code Ekon5140

Credit points 6

Total Hours in Course162

Number of hours for lectures16

Number of hours for seminars and practical classes32

Independent study hours114

Date of course confirmation04.09.2019

Responsible UnitInstitute of Computer Systems and Data Science

reserch
## Līga Paura

Dr. agr.

reserch
## Irina Arhipova

Dr. sc. ing.

Mate5001, Mathematical Statistics

The subject of the statistical and econometrical methods course is the definition and optimal decision making of the following social-economic process problems: forecast methods of probabilistic situation, perspective tasks definition, development goals definition and analysis. Master students study parametric and non-parametric data processing methods, basic principles of choosing parametric and non-parametric statistical methods.

• Knowledge - is able to show the depth or extends knowledge and understanding about parametric and nonparametric data analysis methods (1st test, 1st home work, practical works); Known time series regression analysis. Define economic hypotheses that include the relevant parameters and provide a basis for research, where the estimated parameters are not inconsistent with the fundamental laws of economics. (2nd test, 2nd home work, practical works); is able to apply knowledge of economic research related to the cross discipline fields (1st un 2nd home work, practical works).

• Skills – is able to independently use the theory, methods and problem-solving skills to carry out research activities in the economic evaluation of process parameters, using economic theory or hypothesis formulation (1st un 2nd test, 1st un 2nd home work, practical works). Is able to forcefully explain and discuss the data acquisition, economic theory and forecasting model specification for the particular problem at study (home works).

• Competence – is able to independently formulate and critically analyze the economic hypotheses in master thesis, to evaluate the forecast of economic processes and to interpret the results.

1 Introduction to R, R Studio: read and manipulating data, working with graphics, descriptive statistics[L 1h,P 1h].

2 The Normal distribution. Test for normality[L 1h,P 1h].

3 Parametric comparison of two independent samples – the F- test and t-test[L 1h,P 2h].

4 Non-parametric comparison of two independent samples – the Mann-Whitney tests[L 1h,P 2h].

5 Parametric method for comparing two paired samples – t-test[L 1h,P 2h].

6 Non-parametric method for comparing two paired samples – the Wilcoxon tests[L 1h,P 2h].

7 One-way Anova, two-way Anova[L 0.5h,P 1h].

8 Non-parametric method for comparing two or more independent samples: Kruskal-Wallis test[L 0.5h,P 1h].

9 Contingency tables. Chi-Square Independence Test[L 1h,P 2h].

10 1st test. Nonparametric and parametric methods for comparing two or more samples[P 2h].

11 Forecasting methods. Statistical characteristics of the time series. Moving average methods [L 1h,P 2h].

12 Time series regression analysis [L 1h,P 2h].

13Testing the structural stability of time series [L 2h, P 4h].

14 Time series decomposition methods. Forecasting errors [L 2h,P 3h].

15 Correlation and autocorrelation analysis [L 2h, P 3h].

16 2nd test. Time series [P 2h].

Exam evaluation depends on the semester cumulative assessment: 1st test (40 points), 2nd test (40 points), theory (20 points). 10 points equal to 1 point of exam mark. Test works can be written only at specified time and once.

1st home work: Nonparametric and parametric methods for two and more samples analysis. (Upload in e-system).

2nd home work: Time series (Upload in e-system).

Exam evaluation depends on the semester cumulative assessment (80%) and the theory (20%). The theory examination will be during the session.

Students who have a cumulative assessment of the study course less than 4 or wish to improve it (at least 4) hold the theory or examination. The exam includes practical part (80%) and theory (20%).

1. Arhipova I., Balina S. Statistika ekonomikā un biznesā: risinājumi ar SPSS un MS Excel: mācību līdzeklis. Rīga: Datorzinību centrs, 2006. 359 lpp.

2. Paura L., Arhipova I. Neparametriskās metodes. SPSS datorprogramma: mācību līdzeklis. Jelgava: LLKC, 2002. 148 lpp.

3. Gujarati, Damodar N. Basic econometrics. 3rd ed. New York [etc.]: McGraw-Hill, Inc., 1995. 838 p.

4. Kabacoff R. I. R in action: data analysis and graphics with R. Second edition. Shelter Island, NY: Manning, 2015. 579 P.

1. Jansons V., Kozlovskis K. Ekonomiskā prognozēšana SPSS 20 vidē: mācību grāmata. Rīgas Tehniskā universitāte. 2012.

2. Šķiltere D. Pieprasījuma prognozēšana: Mācību līdzeklis. Rīga: Latvijas Universitāte, 2001.

3. Yaffee Robert A., Monnie McGee. Introduction to Time series Analysis and Forecasting with applications of SAS and SPSS. Academic Press, 2000.

4. Walter Enders. Applied econometric time series. Hoboken, NJ : Wiley, 2004

1. Journal of Econometrics. Elsevier BV. ISSN 03044076

2. Journal of Applied Econometrics. John Wiley and Sons Ltd. ISSN 08837252, 10991255.

3. Computational Statistics and Data Analysis. Elsevier. ISSN 01679473

Obligatory course for master study programme “Ekonomics”