QPE classes

From the second semester of their studies, doctoral students in the QPE programme take classes organised by the Faculty of Economics of the UW. In order to receive information on classes, please contact Mr Piotr Mandera – pmandera@wne.uw.edu.pl.

Classes for which doctoral students must register in other Doctoral Schools (mainly – of Social Sciences) as part of their USOSweb registration. When doing so, students must remember to enrol in an English-speaking group.

1. Acquisition of grants (scholar’s workshop) – 8 compulsory hours plus 4 voluntary hours, 3rd semester

The aim of the classes is to provide the PhD students with knowledge on the principles of constructing research projects financed from external sources. The doctoral students will acquire the skill of effectively drafting grant applications, which will increase the percentage of positively examined applications. Six hours of the classes will be dedicated to the principles of applying for grants and two hours to how they should be settled.

2. Public speaking (scholar’s workshop) – 8 voluntary hours, 3rd semester

The classes aim at increasing the skills of doctoral students in the area of public speaking by teaching them the principles and good practices that should accompany the preparation and delivery of speeches. The docotral student will become familiar with the theory and practice of effective public speaking.

3. Knowledge transfer (scholar’s workshop) – 4 hours, 5th semester

Classes that develop the knowledge sharing skills. Their aim is to become familiar with the possibilities of knowledge transfer from a higher education institution to enterprises, public institutions and non-governmental organisations. The “know-how” is unique and specific for a given organisation (e.g. a higher education institution). The “know what”, on the other hand, includes definitions of concepts, descriptions and professional terminology. The transferring methods may differ depending on the nature of the knowledge to be transferred and its recipients.

Doctoral students are enrolled in these classes by the programme administration (classes 1-3 are organised by programme administration, for classes 4 & 5 – doctoral students are sent to other doctoral schools by the programme administration).

1. QPE programme seminar (semester 1-8)

The seminar is organised for all docotral students fulfilling the QPE programme. It is intended to discuss selected, most important scientific texts and research. Doctoral students take active part in presenting and discussing selected issues. The particular meetings may be conducted by various researchers specialising in the particular research problems under discussion. The whole seminar is coordinated by an employee responsible for the given subject.

The seminar is obligatory for all students of the programme and takes place every week on a day chosen by the doctoral students at the beginning of the academic year. Ultimately, during one meeting, two students present their work or a guest lecturer is invited to the meeting.

2. Quantitative Research Methodology for Social Sciences – 30 hours, 2nd semester

The course focuses on the organization and systematization of knowledge in the field of social sciences methodology. It assumes that students during the first and second degree studies encountered basics of methodology and statistics, and also implemented their own research projects in practice. The first part is an introduction to the philosophical and epistemological basis of scientific research in social sciences. Then, the scientific research process is presented. Emphasis is placed on the measurement methods – the theory of questionnaires and tests, psychophysiological methods and the analysis of Internet data. As a summary, ethics of scientific research is presented, taking into account the importance of reliable data analysis and responsibility for scientific publication.

3. Academic writing – 30 hours, 2nd semester

This workshop will teach PhD students how to successfully communicate the results of their research on a particular topic to the academic audience as well as aiming to help them to improve their writing skills. It will offer its participants the opportunity to work on their own academic papers and develop them from first ideas to early drafts. After choosing the topic of their papers and framing them, the attendees will be assisted in developing a detailed outline of their work by the teacher and through peer support. The students will learn how to properly structure their papers, develop the required sections, select sources and prepare the content. The workshop will also present the features of academic style and the recommended ways of referencing and formatting. The students will be advised how to choose a proper academic outlet and an effective publication strategy for their planned papers. They will learn about the reviewing process to be able to critically assess their work and deal effectively with future reviews.

4. Microeconomics – 30 hours, 1st semester

The aim of the course is to review issues and analytical methods of contemporary microeconomics. The course will show the intuition behind microeconomic assumptions and results. It will also provide practical examples and compare the theory with recent findings in behavioural and experimental economics. Muñoz-Garcia, Advanced Microeconomic Theory, An Intuitive Approach with Examples, the MIT Press, 2017 will serve as a basic text.

5. Methodical workshop on didactics
a. group – 4 hours plus voluntary 4 hours (usually total of 6 hours), 2nd semester
b. individual as part of internship – 4 hours, 2nd semester

The methodical workshops are aimed at preparing the doctoral students for independent teaching of classes in a higher education institution. They consist of:

a. group workshops – class visitations conducted by the best educators. To the extent possible, these classes have different forms (lectures, practical classes, colloquia). The class visitation is carried out under the supervision of the lecturer licensed in pedagogy and encompasses not only participation in classes but also discussion of the classes observed (in total, at least 4 hours, including 2 hours of visitation).

b. individual workshops – at least 2 hours of classes taught by the doctoral students under the supervision and in the presence of an eminent educator or supervisor (plus an hour for joint preparation and an hour to discuss the classes, so this form encompasses in total at least 4 hours of classes counting as didactic internship hours).

Out of the subjects offered, each doctoral student chooses at least 60 hours (at least 1 course).

1. Econometric and statistical modelling – 60 hours

The course will be divided into three parts. The first one will be an applied introduction to R, covering data import, export, saving codes, results, graphics, typical operations in data preparations and analysis, as well as the use of packages. The second part will focus on applied statistical modelling, covering i.a. statistical distributions, Monte Carlo simulations, and bootstrapping. The third part will be devoted to applied econometric modelling, including panel models and spatial models.

2. Microeconometrics – 60 hours

The aim of this course is to familiarize students with the methods used in modern, applied empirical research involving microeconomic data. Topics such as model specification, estimation and inference will be covered for each data type. All lectures will take place in computer lab, and will involve description of the given method and analysis of the case studies to gain a better understanding. This course we will use an open source statistical software, R.

The students at the QPE programme can additionally choose from a list of optional classes. Moreover, they can participate in the lectures offered by all the doctoral schools of the University of Warsaw.

1. Psychology for economists – 60 hours

This course will introduce student to social psychological theory and research results that explain economic behaviour. Topics will include:
• How rational are of human choices?
• Cognitive mechanisms underlying decisions: schema, narratives
• Attitudes
• Social motivation and social emotions
• Self-structure and self-regulation, identity
• How individuals affect each other: social influence and social interdependence
• Group dynamics
• Prejudice and stereotypes
• Social processes, social change
• Social processes and new technology, social processes in social media

2. Choice Modelling – 30 hours

The aim of this course is to familiarize students with the methods used in modern, applied empirical research involving discrete choice data. Topics covered include data sources (revealed, stated preference), design of discrete choice experiments, stated preference survey methods, and econometric treatment. The course will cover both basic and advanced methods used in the literature. The course includes practical computer-based exercises (case studies). This course we will use an open source statistical software, R.

3. Behavioural Decision Science – 30 hours

The aims of this course are to introduce and deepen doctoral students’ understanding of quantitative theories of judgements, decision-making, behavioural science and cognitive psychology. This course will explore theories and research methods in the field of decision-making, judgements, behavioural economics and cognitive psychology. Moreover, the course will promote and encourage critical thinking (and discussions) about fundamental cognitive processes such as memory, categorisation, judgement and choice. The students will critically examine theoretical models of judgement and decision-making, behavioural economics, categorisation and memory. The course will also cover recent developments in behavioural science theories, as well as the impact of experience and categorisation on preference formation and choice. The course will also introduce students to applications of behavioural science, judgement and decision-making research in areas such as finance.

4. Complex systems in economy – 30 hours

The aim of this course is to familiarize students with concepts, methods and tools of complex systems approach and how they explain mechanisms by which simple psychological and social processes drive the dynamics of economic systems. A wide array of research topics will be presented, explaining economic processes using insights from complex systems.

5. Behavioral finance – 15 hours

One of the most important changes that took place in economics at the turn of the 20th and 21st centuries was the increase in the importance of psychology, which contributed to the birth of behavioral finance. Representatives of behavioral finance, intensively using the achievements of psychology, try to explain the irrational economic behavior, especially among stock market investors. During this course, students will learn the basic concepts of behavioral finance, referring to the occurrence of irrational behaviors among investors (the so-called behavioral biases), as well as the stock market anomalies caused by the latter. In addition, students will learn about the latest manifestations of the behavioral trend in economics, specifically whether psychology can help economists to prevent economic crises. Finally, students will discover the link between economics and psychology in the context of searching for a new paradigm in economics.

6. Consumer psychology – 30 hours

The main goal of the course is to understand psychological processes underlying consumer behavior, such as emotions, motivation, and cognitive processes. During the course consumer behavior will be analyzed from two perspectives:
a) psychological knowledge underlying consumer behavior (basic psychological processes, e.g. emotion, motivation, cognitive processes, etc.);
b) automatic and unconscious processes accompanying each stage of consumer decision-making processes.

7. Data science – 15 hours

The aim of this course is to give a knowledge of and facility with modern methods of multivariate statistical data analysis: methods of density estimation, multiple regression and modern shrinkage methods for variable selection (e.g. ridge regression and LARS), cross validation techniques: dimensionality reduction (Principal Components, Canonical Correlation), discriminant analysis, modern methods for cluster analysis support vector machines. The techniques will be implemented on a wide variety of data sets using the statistical software package “R”.

8. Experimental economics – 30 hours

This course will cover a number of key questions and techniques of experimental economics.

9. Responsible machine learning – 30 hours

The aim of the classes is to familiarize participants with the issues of responsible machine learning. This will allow for the correct design of the machine learning experiment. In particular, we will discuss the basic techniques of explainable machine learning and the measures used in the assessment of fairness. In addition to seminar-type meetings, examples using R and Python will be presented.