Programming Analytical Methods 2400-M1IiEPNA
To propose a novel econometric or statistical tool there is a necessity of programming it. More often than not, in academic work or data analysis at work existing programms adjustments appears. Because of atypical data features, econometricians have to propose a novel method to handle them. The purpose off this class is to recollect and combine students' knowledge of statistics, undergraduate econometrics.
Topics list (lista tematow)
(1) Introduction to R, basic calculations, creating vectors and matrices.
(2) Programming basic statistical tests.
(3) Programming the Ordinary Least Squares Method.
(4) Introduction to the Maximum Likelihood Method.
(5) Likelihood plots. Maximizing one parameter log-likelihood functions.
(6) Maximizing multiparameter log-likelihood functions using their gradients and Hessian. 3D plots.
(7) Introduction to the Method of Moments.
(8) Introduction to the Generalised Method of Moments.
(9) The Generalised Method of Moments estimation.
(10) Colloquium
(11) Bootstrap
(12)-(15) Monte Carlo Method and experiments.
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.
2. Students knows basic techniques and information technology tools.
3. Student knows selected analytical and computational tools out of econometrician's toolbox.
B) Abilities
Student can use statistical and econometric environments, create their own functions and programms, and adapt procedures created by other scientists and co-workers.
1. Student can perform data analysis with basic statistical software.
2. Student is able to create their own computer functions and scripts.
3. Student can prepare a function or a programm that executes nonclassical data analysis.
4. Student is prepared to work with basic data formats and structures.
5. Student can apply analytical methods for problem solving.
6. Student can make use of computer procedures created by other parties.
7. Student can adequately choose analytical tool for an economic, financial, or related problems.
8. Student has the ability of executing a series of computational and analytical operations.
9. 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 extend their knowledge single-handedly.
3. Student can make use of programms prepared by others and create functions understandable and useful for their team-members.
4. Student can assess usefulness of a selected tool for a given problem solving.
5. Student understands limitations of computer techniques in analysing complicated economic phenomenons.
KW01, KW02, KW03, KW04, KU01, KU02, KU03, KU04, KU05, KU06, KU07, KK01, KK02, KK03
Assessment criteria
Final grade is a weighted average of class attendance (20%), fully announced quizzes (20%), class work (20%), colloquium (20%), and final project (20%).
Bibliography
1. Materiały przygotowane przez prowadzącego
2. Givens G.H., Hoeting J.A., Computational Statistics, John Wiley & Sons, 2012
3. Laura M. Chihara,Tim C. Hesterberg, Mathematical Statistics with Resampling and R, John Wiley & Sons, 2011
4. Mycielski J., Skrypt do Ekonometrii, Wydział Nauk Ekonomicznych
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: