Econometrics, econometrics and more money 2400-PL3SL283B
The Classical Linear Regression model will be used to answer both the classic questions of economics and unordinary hypotheses‘ verification. All students interested in econometrics are welcome. It is always great to have your own idea for research or know what method is of interest, however it is no necessary in this seminar. Frequency of meetings will be settled with all participants.
The seminar should end up with a thesis which after minor corrections may be submitted to the Dean’s Office.
Topics
1. Programming new and advanced econometric tools and methods
2. How outliers affect regression results (robust regression)
3. Seemingly Unrelated Regressions (SUREG)
4. The Bootstrap in econometrics and small sample regression models
5. Endogeneity and the instrumental variables method
6. Time Series Analysis with the Kalman Filter
7. Kernel estimations for regression models
8. Additional, including more advanced, topics that are of interest to participants
The topic selection is not limited to regression models and has to fit into the thesis; goal.
Type of course
Course coordinators
Learning outcomes
A) Knowledge
Student has basic knowledge of creating novel computer functions and programms for statistical and econometric purposes.
1. Student knows advantages and disadvantages of using computer programms for data analysis.
B) Abilities
Student can perform an econometric analysis to verify hypotheses.
1. Student can perform data analysis with basic statistical software.
2. Student can adequately choose analytical tool for an economic, financial, or related problems.
3. Student has the ability of executing a series of computational and analytical operations.
4. Student is prepared to analyse critically results, interpret their economic sense, and create clear reports.
C) Social competences
Student is aware of necessity of self-improvement and life-long-learning.
1. Student can present data in a clear and understandable way.
2. Student is prepared to stretch the range of konwledge independently.
3. Student can assess usefulness of a selected tool for a given problem solving.
Assessment criteria
The participants will be graded on the basis of their advances in thesis’ preparation.
Bibliography
1. Vance Martin, Stan Hurn i David Harris, Econometric Modelling with Time Series. Specification, Estimation and Testing, Cambridge University Press, 2013
2. Jeffrey Wooldridge, Introductory Econometrics, A Modern Approach, 7e, Cengage, 2019
3. Christopher Baum, An Introduction to Modern Econometrics Using Stata, Stata Press, 2006
4. Bradley Efron, Robert Tibshirani, An Introduction to the Bootstrap, Chapman & Hall CRC Press, 1993
5. Michael D. Ward, John S. Ahlquist, Maximum Likelihood for Social Science: Strategies for Analysis, Cambridge University Press, 2019
6. Scott Cunningham, Causal Inference: The Mixtape: The Mixtape, Yale University Press, 2021
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: