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Economic Models

Code: 40097 ECTS Credits: 15
2025/2026
Degree Type Year
Economic Analysis OB 1

Contact

Name:
Amedeo Stefano Edoardo Piolatto
Email:
amedeo.piolatto@uab.cat

Teachers

Michael David Creel
Jordi Masso Carreras
(External) Lidia Farré

Teaching groups languages

You can view this information at the end of this document.


Prerequisites

No specific prerequisits.


Objectives and Contextualisation

The goal of the first part of the module is for students to learn standard concepts of non-cooperative and cooperative Game Theory at a graduate level.

In the second and third parts of the module the goal is for students to learn how  to  analyze,  interpret  and  organize  economic  data  with  advanced  statistical  and  econometric techniques.  The  student will also become familiar with the use of econometric software  packages.     


Competences

  • Apply the methodology of research, techniques and specific advanced resources to research and produce innovative results in a specific area of specialisation
  • Capacity to articulate basic economic theory, analytically deriving them from mathematical reasoning
  • Capacity to identify basic statistical analysis and econometric techniques deriving them from the laws of probability and statistics
  • Conceptually analyse a specific economic problem using advanced analytical tools
  • Find, compile and analyse economic data using advanced econometric techniques
  • Possess and understand knowledge that provides a basis or opportunity for originality in the development and/or application of ideas, often in a research context
  • Student should possess the learning skills that enable them to continue studying in a way that is largely student led or independent
  • Students should know how to apply the knowledge they have acquired and their capacity for problem solving in new or little known fields within wider (or multidisciplinary) contexts related to the area of study
  • Students should know how to communicate their conclusions, knowledge and final reasoning that they hold in front of specialist and non-specialist audiences clearly and unambiguously
  • Use new technology for the collection and organisation of information to solve problems in professional activities
  • Use the main computer packages to program economic data analysis

Learning Outcomes

  1. Apply the methodology of research, techniques and specific advanced resources to research and produce innovative results in a specific area of specialisation
  2. Critically analyse the different estimators and basic empirical methods
  3. Describe the underlying basis for modelling dynamic economic phenomena on a macroeconomic scale
  4. Frame an economic question in a mathematical problem and derive the answer using mathematical logic
  5. Identify the possibilities and limitations of basic empirical analysis
  6. Implement an empirical analysis with all its stages using publicly accessible data bases
  7. Possess and understand knowledge that provides a basis or opportunity for originality in the development and/or application of ideas, often in a research context
  8. Program basic methods of estimation.
  9. Student should possess the learning skills that enable them to continue studying in a way that is largely student led or independent
  10. Students should know how to apply the knowledge they have acquired and their capacity for problem solving in new or little known fields within wider (or multidisciplinary) contexts related to the area of study
  11. Students should know how to communicate their conclusions, knowledge and final reasoning that they hold in front of specialist and non-specialist audiences clearly and unambiguously
  12. Use new technology for the collection and organisation of information to solve problems in professional activities

Content

I.Game Theory

1.Introduction to Game Theory and Some Examples

2.Games in Normal Form

3.Games in Extensive Form

4.Nash Equilibrium and Related Issues

5.Repeated Games

6.Games of Incomplete Information

7.Bargaining Theory

8.Cooperative Games

 

II.Econometrics  I  

1. Introduction to econometric analysis

2. Ordinary least squares

3. OLS and finite sample theory

4. OLS and large sample theory

5. Nonspherical disturbances

6. Endogeneity

 

III.Econometrics  II  

1. Extremum estimation and numerical optimization

2. Maximum likelihood

3. Generalized Method of Moments  

4. Introduction to  time series  analysis  

5. Additional topics  in econometrics  

For a detailed description of the content of this module go to https://sites.google.com/view/idea-program/master-program .

 


Activities and Methodology

Title Hours ECTS Learning Outcomes
Type: Directed      
Theory classes 112.5 4.5 2, 1, 3, 4, 5, 6, 8, 10, 11, 9, 7, 12
Type: Supervised      
Problems sets, tutorials 75 3 2, 1, 3, 4, 5, 6, 8, 10, 11, 9, 7, 12
Type: Autonomous      
Personal study, study groups, textbook readings, article readings 187.5 7.5 2, 1, 3, 4, 5, 6, 8, 10, 11, 9, 7, 12

The course will consist of sessions where the instructor presents the material, and sessions specifically dedicated to problem solving. Students are encouraged to form study groups to discuss assignments and readings.

The proposed methodology may undergo some modifications according to the restrictions imposed by the health authorities on on-campus courses.

 

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.


Assessment

Continous Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Class Attendance and Problem sets and assignments 22% 0 0 2, 1, 3, 4, 5, 6, 8, 10, 11, 9, 7, 12
Exam Part I 26% 0 0 2, 1, 3, 4, 5, 6, 8, 10, 11, 9, 7, 12
Exam Part II 26% 0 0 2, 1, 3, 4, 5, 6, 8, 10, 11, 9, 7, 12
Exam Part III 26% 0 0 2, 1, 3, 4, 5, 6, 8, 10, 11, 9, 7, 12

1. CONTINUOUS EVALUATION

  Exam Part I

26%  

 Exam Part II

26%  

 Exam Part III

26%  

Problem  sets,   assignments  & Class  attendance    and    active    participation

22%  

The proposed evaluation activities may undergo some changes according to the restrictions imposed by the health authorities on on-campus courses. 

In this course, the use of Artificial Intelligence (AI) technologies is not permitted in any of its phases. Any work that includes fragments generated with AI will be considered a breach of academic honesty and may result in a partial or total penalty to the activity's grade, or more severe sanctions in serious cases.

2. THIS MODUL CONTEMPLATES A COMPREHENSIVE EVALUATION option:

COMPREHENSIVE EVALUATION (Art. 265 of the UAB Academic Regulations)

By requesting the comprehensive evaluation the student waives the option of continuous evaluation.

The comprehensive  evaluation must be requested at the Academic Management (Gestió acadèmica) of the Campus where the degree/master's degree is taught. The request must be filed according to the procedure and the deadline  established by the administrative calendar of the Faculty of Economics and Business.

Attendance :

  • Student  attendance  is mandatory on the day of the comprehensive assessment. The date will be the same as that of the final exam of the semester as per the evaluation calendar published by the Faculty of Economics and Business and approved by the Faculty's Teaching and Academic Affairs Committee. The duration of the comprehensive assessment must be specified in the characteristics of such activity.
  • 100% of the evaluation evidences must be handed in by the student on the day of the comprehensive assessment.
  • The evaluation evidences carried out in person by the student on the same day of the comprehensive assessment must have a minimum weight of 70%.

The following information referring to the characteristics of the comprehensive assessment must be included. We suggest incorporating the following table:

Evidence Type (1)

Weight in the final assessment (%) (2)

Duration of the activity

Is the activity that corresponds to this evaluation evidence to be carried out in person on the  date scheduled for the comprehensive evaluation? (YES/NO) (3)

EXAM

80%

 

 YES

 LAB TEST

 20%

 

 YES

 

 

 

 

TOTAL

100%

 

 

 

(1)    Descriptive title of each piece of evidence (exam, problem sets solving, case analysis, activity carried out using specific software that the student is expected to know,...)

(2)    Weight of the evidence in the final mark of the subject (specify the percentages of each evaluation evidence that the student must undertake)

(3)    For each piece of evidence: Is the activity that corresponds to this evaluation evidence to be carried out in person on the  date scheduled for the comprehensive evaluation? (YES/NO)


Bibliography

Game theory:

Fudenberg and J. Tirole (1991). Game Theory. MIT Press.

Gibbons (1992). A Primer in Game Theory. Harvester Wheatsheal.

Luce and H. Raiffa (1957). Games and Decisions. Wiley.

Mas-Colell, M. Whinston and J. Green (1995). Microeconomic Theory. Oxford University Press.

Moulin (1986). Game Theory for the Social Sciences (second edition). New York University Press.

Moulin (1988). Axioms of Cooperative Decision Making. Cambridge University Press (Econometric Society Monographs).

Myerson (1991). Game Theory: Analysis of Conflict. Harvard University Press.

Osborne and A. Rubinstein (1994). A Course in Game Theory. MIT Press.

Owen (1982). Game Theory (second edition). Academic Press.

Shubik (1984). Game Theory in the Social Sciences. MIT Press.

Vega-Redondo (2003). Economics and the Theory of Games. Cambridge University Press.

 

Econometrics I and II

Cameron, A.C. and P.K. Trivedi, Microeconometrics - Methods and Applications

Davidson, R. and J.G. MacKinnon, Econometric Theory and Methods

Gallant, A.R., An Introduction to Econometric Theor

Greene, W.H. Econometric Analysis, Pearson Prentice Hall.

Hamilton, J.D., Time Series Analysis

Hayashi, F.,Econometrics, Princeton Univesrity Press.

Wooldridge. Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge- Mass, USA.

 

Additional references will be provided during the course.


Software

  • Matlab
  • R
  • Phyton
  • Stata

Groups and Languages

Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.

Name Group Language Semester Turn
(PLABm) Practical laboratories (master) 30 English second semester morning-mixed
(TEm) Theory (master) 30 English second semester morning-mixed