Micro data analysis 1600-SZD-SPEC-DPJ-EF
The aim of this course is to familiarize students with the methods used in a modern, empirical research involving microeconomic data. Topics such as model specification, estimation and inference will be covered for different data types. All workshops will take place in a computer lab (in the case of the in-person format), and will involve description of the given method, analysis of the case studies, and in-class exercises. During this course we will use an open source statistical software, R. The exact curriculum will depend on the participants’ interests and previous knowledge of microeconometric methods. The broad overview of the potential topics include: (1) Introduction to R software. Basics of data handling. (2) Ordinary Least Squares. (3) Generalization of the linear model: heteroskedasticity, non-linear functional form, and quantile regression. (4) Generalized linear models. (5) Endogeneity and two stage least square method. (6) Sample selection. (7) Maximum likelihood estimation (MLE): Binary models. (8) Inference with MLE: testing hypothesis, marginal effects, elasticities. (9) Random utility model and models for multinomial variables. (10) Models for ordinal variables and count data. (11) Extensions for panel data.
Type of course
Course coordinators
Learning outcomes
Knowledge | The graduate knows and understands:
WG_03 - scientific research methodology in the field of the social sciences
Skills | The graduate is able to:
UK_05 - speaking a foreign language at B2 level of the Common European Framework of Reference for Languages using the professional terminology specific to the discipline within the social sciences, to the extent enabling participation in an international scientific and professional environment
Social competences | The graduate is ready to
KO_01 - fulfilling the social obligations of researchers and creators
And others: Participants of the course will be familiarized with methods and tools of microeconometrics – both theoretical (rationale, assumptions, theory) and practical (building a model, data analysis, estimation, interpretation of the results).
Assessment criteria
It is recommended that students know and understand the basic concepts related to statistics, probability theory, and mathematical analysis. Due to the practical nature of the course, it is not necessary, but it will make it easier to understand the issues discussed during the course, otherwise it will require filling the gaps on an ongoing basis.
Principles for passing the classes and the subject (including resit session): Passing the course will be based on the in-class exercises and workbooks that participants will have to complete at home. It will involve applying tools and methods taught in class to the new datasets.
Methods for the verification of learning outcomes: In-class exercises and workbooks (home assignments).
Evaluation criteria: In order to pass the course, participants are required to complete 4 out of 5 workbooks (1 for each meeting).
Bibliography
(1) Greene, W. H., 2011. Econometric Analysis. 7 Ed., Prentice Hall. (2) Cameron, A. C., and Trivedi, P. K., 2005. Microeconometrics: Methods and Applications. Cambridge University Press. (3) Train, K. E., 2009. Discrete Choice Methods with Simulation. 2 Ed., Cambridge University Press, New York. (4) Hensher, D. A., Rose, J. M., and Greene, W. H., 2015. Applied Choice Analysis. 2 Ed.,Cambridge University Press, Cambridge. (5) Greene, W. H., and Hensher, D. A., 2010. Modeling Ordered Choices: A Primer. Cambridge University Press.
Notes
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Term 2024L:
The classes will take place remotely. |
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: