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Econometrics

Course developer

Prior knowledge

Ekon2122, Statistics

Ekon3114, Economic Research

Mate2023, Mathematics for Economists

Mate2030, Mathematical Statistics

Course abstract

The aim of the econometrics course is to test economic hypotheses that include the relevant parameters and to make sure that the assessed parameters do not contradict the fundamental laws of economics. The task of the econometrics course is to evaluate the parameters of economic processes using the formulation of economic theory or hypothesis, data acquisition, the specification of the mathematical model of economic theory or hypothesis and the specification of the econometric model. When testing model assumptions and corresponding hypotheses, the model is used in forecasting.

Learning outcomes and their assessment

Knowledge:
1. is able to show an understanding of the most important concepts and regularities of econometrics. Knows the model of one-factor linear regression analysis and a measure “goodness of fit” - 1st test, practical works.
2. knows the functional forms of regression models, is able to put forward economic hypotheses that include the relevant parameters that do not contradict the fundamental laws of economics - 2nd test and practical works.
3. knows multiple regression models and their application – 3rd test and practical works.
Skills:
1. is able to independently use theories, methods and problem-solving skills to evaluate the parameters of economic processes, using the formulation of economic theory or hypothesis - 1st test, 2nd test, 3rd test and practical works.
2. is able to independently structure his / her learning and show a scientific approach to the development of a specific econometric model in problem solving -practical works.
Competence:
1. is able to independently obtain, select and analyse information and use it, make decisions and solve problems in the field of econometrics.
2. is able to independently formulate and critically analyse economic hypotheses, to evaluate the parameters of economic processes, to interpret the obtained results.

Course Content(Calendar)

1. Introduction to econometrics. Experimental and observational data. Types of data: time-series, cross-section and panel data. Methodology of econometrics [Lectures - 1h, Practical works - 1h].
2. One-factor or two variable linear regression model. The population and sample regression function. Coefficient of determination: a measure “goodness of fit”. Sample correlation coefficient [Lectures - 1h, Practical works - 1h].
3. Classical normal linear regression model (CNLRM) and its assumptions. Significance of the regression parameters. Hypothesis testing: confidence-interval approach, p-value and test of significance [Lectures - 2h, Practical works - 4h].
4. 1st test: Simple linear regression. Hypothesis testing [Practical works - 2h].
5. Functional forms of the linear regression model. Power or double-log model. Semilog models: exponential and logarithmical models. Reciprocal or hyperbolic model [Lectures - 2h, Practical works - 6h].
6. 2nd test: Functional forms of the linear regression model. Hypothesis testing [Practical works - 2h].
7. Multiple regression model and its assumptions. Hypothesis testing. Models comparison [Lectures - 2h, Practical works - 6h].
8. 3rd test: Multiple regression model. Hypothesis testing [Practical works - 2h].

Requirements for awarding credit points

Exam evaluation depends on the semester cumulative assessment: 1st test (20 points), 2nd test (20 points), 3rd test (20 points), exam (40 points). 10 points equal to 1 point of exam mark. Test works can be written only at specified time and once. The processing of missed classes takes place in accordance with the LLU regulatory framework.

Description of the organization and tasks of students’ independent work

To acquire and strengthen the issues of theory by studying literature, solving tasks. To prepare for tests and exam.

Criteria for Evaluating Learning Outcomes

Exam evaluation depends on the test’s cumulative assessment (60%) and exam (40%). The student takes the exam within the specified examination time during the session. The assessment can be improved in accordance with the LLU regulatory framework.

Compulsory reading

1. Arhipova I., Balina S. Statistika ekonomikā un biznesā: risinājumi ar SPSS un MS Excel: mācību līdzeklis. Rīga: Datorzinību centrs, 2006. 359 lpp.
2. Krastiņš O. Ekonometrija. Rīga: LR Centrālā statistikas pārvalde, 2003. 207 lpp.
3. Gujarati D. 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 Pp. .

Further reading

1. Gujarati D. N. Essentials of Econometrics. 2nd ed. Boston [etc.]: McGraw-Hill, Inc., 1999. 534 p.
2. Studenmund A. H. Using econometrics: a practical guide. 5th ed. Boston : Addison Wesley Pearson, 2006. 369 p.

Periodicals and other sources

1. Centrālā statistikas pārvalde (CSP). Pieejams: http://www.csb.gov.lv
2. Eurostat. Pieejams: http://ec.europa.eu/eurostat

Notes

Compulsory course for Bachelor’s study programme “Economics”.