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2020/2021

Economic Models

Code: 40097 ECTS Credits: 15
Degree Type Year Semester
4313805 Economic Analysis OB 1 2
The proposed teaching and assessment methodology that appear in the guide may be subject to changes as a result of the restrictions to face-to-face class attendance imposed by the health authorities.

Contact

Name:
Jesús David Pérez Castrillo
Email:
David.Perez@uab.cat

Use of Languages

Principal working language:
english (eng)

Teachers

Jordi Massó Carreras
Pau Milan Sole

External teachers

André Gröger

Prerequisites

No specific prerequisits.

Objectives and Contextualisation

This  module  seeks  two  main  objectives:

On  the  one  hand,  the course covers the basic and standard concepts of non-cooperative and cooperative Game Theory at a graduate level.

On the other it  teaches  students  how  to  analyze,  interpret  and  organize  economic  data  with  advanced  econometric  and  statistical  techniques.  In part two it  shows  students  how  to  use  advanced econometric  techniques  and  theoretical  models  to  make  economic  forecasts  and  therefore,  be  able  to  evaluate  important  economic  policies.  The  student  also  learns  how  to  use  the  main software  packages  necessary  for  data  analysis.     

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

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

 

Econometrics  I  

1. Causal  inference  vs.  forecasting  and  types  of  data  

2.  Conditional  expectations  and  their  properties  

3. Identification,  estimation,  and  inference  in  bivariate  OLS  regression  

4. Identification,  estimation,  and  inference  in  multiple  OLS  regression  

5. Measurement  error  bias  and  solutions  

6. Sample  selection  bias  and  solutions  

7. Reverse  causality  bias  and  solutions  

8. Standard  error  bias  and  solutions  

9. Identification,  estimation,  and  inference  in  linear  IV  regression  

10. Weak  instrument  bias  and  size  distortion  

11. Extremum  estimator  

 

Econometrics  II  

12. Maximum  likelihood  

13. Generalized  Method  of  Moments  

14. Introduction  to  time  series  analysis  

15. Additional  topics  in  econometrics  

 

Methodology

• Theory  classes    

• Practice  classes    

• Learning  based  on  problem  solving  

• Tutorials  

• Personal  study    

• Study  groups    

• Textbook  reading    

• Article  reading   

Activities

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

Assessment

 

Final  Exams    

50%  

Class  attendance    and    active    participation    

20%  

Problem  sets    and    assignments    

30%  

A module consists of different courses which are evaluated through final exams, problem sets and assignments and other class activities such as class attendance, presentations, etc.

 
   
   

Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Class Attendance and Problem sets and assignments 50% 0 0 2, 1, 3, 4, 5, 6, 8, 10, 11, 9, 7, 12
Final Exams 50% 0 0 2, 1, 3, 4, 5, 6, 8, 10, 11, 9, 7, 12

Bibliography

D. Fudenberg and J. Tirole (1991). Game Theory. MIT Press.
R. Gibbons (1992). A Primer in Game Theory. Harvester Wheatsheal.
R. Luce and H. Raiffa (1957). Games and Decisions. Wiley.
A. Mas-Colell, M. Whinston and J. Green (1995). Microeconomic Theory. Oxford University Press.
H. Moulin (1986). Game Theory for the Social Sciences (second edition). New York University Press.
H. Moulin (1988). Axioms of Cooperative Decision Making. Cambridge University Press (Econometric Society Monographs).
R. Myerson (1991). Game Theory: Analysis of Conflict. Harvard University Press.
M.J. Osborne and A. Rubinstein (1994). A Course in Game Theory. MIT Press.
G. Owen (1982). Game Theory (second edition). Academic Press.
M. Shubik (1984). Game Theory in the Social Sciences. MIT Press.
F. Vega-Redondo (2003). Economics and the Theory of Games. Cambridge University Press.

Hayashi (2001) Econometrics, Princeton Univesrity Press.
Cameron and Trivedi, (2005) Microeconometrics: Methods and Applications, Cambridge University Press.
Wooldridge (2002) Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge- Mass, USA.
Greene, W.H. (2000) Econometric Analysis, Pearson Prentice Hall.
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 Theory
Hamilton, J.D., Time Series Analysis
Hayashi, F., Econometrics