Guide in PDF format
Logo

Advanced Microeconomics

Code: 45770
Credits: 10
2026/2027
Degree programme Type Course
Economic Analysis OB 2

Contact lecturer

Name :
Pau Milán Solé
Email :
pau.milan@uab.cat

Teaching staff

Marta Troya Martinez
Fernando Payro Chew
Peter Bayer
David Jesus Andres Cerezo
Amedeo Piolatto
Pau Milán Solé
Mikhail Drugov Plitman

Teaching staff (external to UAB)

Christopher Rauh
Mar Reguant

Group languages

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

Prerequisites

No specific prerequisits.

Objectives

This   module   covers    advanced    theoretical    models     that     are   in   the   frontier     of   modern   microeconomic   analysis.    Using     rigorous   mathematical   analysis,   this    module  provides   the   student   with     frontier   knowledge   in     contract  design    and game theory,   as   well   as   in   different    applications.   The   models   seen   in   this   module   are   applied   in  novel     research     in     Microeconomics   and are instrumental  to obtaining public   policy   implications. The student can acquire this  knowledge from the  fields of economics of  information, industrial organization,  contracts, incentives and behaviour, corporate  finance, health economics,  development economics, microeconometrics, public economics,labor economics, asset pricing, political economics and experimental economics.

Learning outcomes

  • CA18 (Identify market strategies according to their impact on competition and welfare.) Identify market strategies according to their impact on competition and welfare.
  • CA19 (Test the consistency of industrial organisation models using simulations.) Test the consistency of industrial organisation models using simulations.
  • CA20 (Evaluate the impact of sectoral regulations with asymmetric information models.) Evaluate the impact of sectoral regulations with asymmetric information models.
  • KA30 (Recognise contract theory and associated incentives.) Recognise contract theory and associated incentives.
  • KA31 (Exemplify the information economy in scenarios of moral hazard and adverse selection.) Exemplify the information economy in scenarios of moral hazard and adverse selection.
  • KA32 (Identify the theory of industrial organisation and models of imperfect competition.) Identify the theory of industrial organisation and models of imperfect competition.
  • KA33 (Explain the strategic relationships between companies with dynamic game theory.) Explain the strategic relationships between companies with dynamic game theory.
  • KA34 (Identify evidence of adverse selection in insurance markets.) Identify evidence of adverse selection in insurance markets.
  • SA26 (Analyse multi-period strategic decisions with asymmetric information.) Analyse multi-period strategic decisions with asymmetric information.
  • SA27 (Compare principal-agent models under different risk assumptions.) Compare principal-agent models under different risk assumptions.
  • SA28 (Integrate game theory and industrial analysis to evaluate antitrust policies.) Integrate game theory and industrial analysis to evaluate antitrust policies.
  • SA29 (Experiment with auction simulations to test bidding strategies.) Experiment with auction simulations to test bidding strategies.
  • SA30 (Classify vertical restraints according to their effect on competition.) Classify vertical restraints according to their effect on competition.
  • SA31 (Structure algorithms of evolutionary dynamics to study oligopolistic competition.) Structure algorithms of evolutionary dynamics to study oligopolistic competition.

Contents


  • Behavioural Economics

  • Corporate Finance

  • Data

  • Economics of Information

  • Energy

  • Industrial Organisation

  • Industrial and Energy Economics

  • Networks

  • Public Economics


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

Learning activities and methodology

Title Hours ECTS Learning outcomes
Theory classes 75 3
Practice classes, problems sets, tutorials 25 1
Learning based on problem solving, personal study, study groups 150 6

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 teaching 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

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Midterm Exam 26% 0 0 CA18, CA19, CA20, KA30, KA31, KA32, KA33, KA34, SA26, SA27, SA28, SA29, SA30, SA31
Midterm Exam 26% 0 0 CA18, CA19, CA20, KA30, KA31, KA32, KA33, KA34, SA26, SA27, SA28, SA29, SA30, SA31
Class Attendance and Problem sets and assignments 22% 0 0 CA18, CA19, CA20, KA30, KA31, KA32, KA33, KA34, SA26, SA27, SA28, SA29, SA30, SA31
Midterm Exam 26% 0 0 CA18, CA19, CA20, KA30, KA31, KA32, KA33, KA34, SA26, SA27, SA28, SA29, SA30, SA31

This modul does not contemplate an evaluation from a single comprehensive exam

Midterm  Exam

26%  

 Midterm Exam

26%  

 Midterm Exam

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.

 

 

Bibliography

Aghion, P. and Bolton, P. (1992), “An Incomplete Contracts Approach to Financial Contracting,” Review of Economic Studies 59: 473-494.

Akerlof, G. (1970), “The Market for Lemmons, Quality Uncertainty and the Market Mechanism,” Quarterly Journal of Economics 84: 488-500.

Akerlof, G.A., Kranton, R.E., 2000. Economics and Identity. Q. J. Econ. 115, 715—753

Allen, F. and Michaely, R. (1995), “Dividend Policy,” in Handbooks of Operation Research and Management Science: Finance (ed. Jarrow, R., Maksimovic, V. and Ziemba, W.), Amsterdam: North-Holland.

Amemiya, (1985), Advanced Econometrics, Blackwell

Angrist, J. D. and J.-S. Pischke (2009), Mostly Harmless Econometrics, An Empiricist ´s Companion, Princeton University Press.

Bisin, A., Verdier, T., 2001. The Economics of Cultural Transmission and the Dynamics of Preferences. J. Econ. Theory 97, 298--319.

Brealey, R.A. and Myers, S.C. (1997), “Principles of Corporate Finance,” New York McGraw-Hill.

Cameron, A. C. and P. K. Triverdi (2005), Microeconometrics: Methods and Applications, Cambridge University Press

Carvalho, J.-P. (2013) \"Veiling\" Quarterly Journal of Economics 128(1); 337-370

Caselli, F.and W. J. Coleman, II, On The Theory of Ethnic Conflict, Journal of the European Economic Association, Volume 11, Issue suppl_1, 1 January 2013, Pages 161-192

Davis P. and E. Garcés, Quantitative Techniques for Competition and Antitrust Analysis, Princeton University Press

Jackson, M. O. Social and economic networks. Princeton University Press,2010.

Newman, M.. Networks: an introduction. Oxford University Press, 2009.

Shum, M. Econometric Models of Industrial Organization, World Scientific

Tirole, J. (2006), “The Theory of Corporate Finance,” Princeton University Press.

Tirole, J. The Theory of Industrial Organization, The MIT Press

Toke, A. , F. Albornoz and E. Hauk (2021) “Foreign influence in domestic policy”, Journal of Economic Literature 59(2):426-87

Víctor Aguirregabiria's notes (University of Toronto, Department of Economics)

Wooldridge, J. M. (2002), Econometric Analysis of Cross Section and Panel Data, MIT Press

 

Additional references will be provided during the course.

Software

  • Matlab
  • R
  • Phyton
  • Stata

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE