| Course Title | Econometrics |
| Course Code | D-052-01 |
| Level | PhD |
| ECTS Credits | 6.00 |
| Faculty/unit | Engineering Faculty |
| Field | Matemātika |
| Course Type | Standard |
| Course Leader | Pavlyuk Dmitry - Dr. sc. ing. professor |
| Annotation | This study course provides a comprehensive introduction to econometric data analysis, covering a range of topics related to sampling, validity, reliability, causality, qualitative and quantitative methods. Students will learn about the principles of sampling design, including considerations such as target population and sample representativity, as well as the classification of sampling methods. The course also covers the structure of data, levels of measurement, and scales, as well as the differences between categorical and continuous data and non-response bias. Students will explore the foundations of estimation and inference, including design-based inference, confidence intervals, variance estimation, and hypothesis testing, and will also learn about regression models, discrete choice models, and estimation methodology. The course provides practical aspects of longitudinal survey data analysis, time series, and macroeconometrics, and also covers the process of carrying out an empirical project, from posing a question to econometric analysis. |
| Aim | The aim of this course is to provide students with a comprehensive understanding of the econometric data analysis process, from sampling design and data structure to estimation methodology and carrying out an empirical project, using both qualitative and quantitative methods to ensure validity, reliability, and causality. |
| LO1 | Ability to design and implement appropriate sampling methods and evaluate the representativity of their samples for a given target population. |
| LO2 | Ability to distinguish between categorical and continuous data and identify potential non-response bias, and be able to clean and preprocess data for effective econometric analysis. |
| LO3 | Ability to apply appropriate estimation and inference techniques, including regression models, discrete choice models, and panel regression models, to analyze and interpret data, and to carry out an empirical project from posing a research question to summarizing and presenting their results. |
| Required Literature | - Greene, W.H. (2018), Econometric Analysis, 8th edition., Pearson, New York, NY.
- Wooldridge, J.M. (2018), Introductory Econometrics: A Modern Approach, 7th edition., Boston, MA.
- Huynh, KP, Jacho-Chavez, DT, & Tripathi, G (eds) 2019, The Econometrics of Complex Survey Data: Theory and Applications, Emerald Publishing Limited, Bingley. Available from: https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=5743727 .
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| Assessment Method | In the frame of the course project/course work, students independently execute the tasks, considering the published schedule. The result of the execution is a report, which is graded, using a prior published grading scale. Considering assessment results the grade becomes available to student with the feedback.
study the provided methodological and other materials, which are posted in e.tsi.lv. They independently perform tasks, prepare and draw up a report according to the requirements, form a list of questions that are discussed during classes with the teacher. |
| Independent study | As part of the course project/course work, students independently study the provided methodological and other materials, which are posted in e.tsi.lv. They independently perform tasks, prepare and draw up a report according to the requirements, form a list of questions that are discussed during classes with the teacher. |
| Full-time |
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| First Sit Elements | Element Weighting, % | Group Work | Links to Results |
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| Reports | 30 | | LO1, LO2, LO3 | | Presentations | 30 | | LO1, LO2, LO3 | | Examination | 40 | | LO1, LO2, LO3 |
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