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Applied and Quantitative Economics

Code: 45771
Credits: 10
2026/2027
Degree programme Type Course
Economic Analysis OB 2

Contact lecturer

Name :
Marta Troya Martinez
Email :
marta.troya@uab.cat

Teaching staff

Luca Salvadori
Pierre Magontier
David Jesus Andres Cerezo
Caterina Muratori
Luca Gambetti

Teaching staff (external to UAB)

Christopher Rauh
Mar Reguant
Joan Llull Cabrer

Group languages

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

Prerequisites

No specific prerequisits.

Objectives

This module provides students with advanced econometric techniques for analyzing micro and macro data. These techniques can be applied to (and be learned from) the areas of Health economics, labor economics, public economics, experimental economics, empirical finance, trade and International economics, development economics and political economy. 

 

Learning outcomes

  • CA21 (Compare health or policy strategies with empirical evidence.) Compare health or policy strategies with empirical evidence.
  • CA22 (Design empirical strategies adjusted to the problem and available data.) Design empirical strategies adjusted to the problem and available data.
  • CA23 (Run analyses with specialised software documenting the entire process.) Run analyses with specialised software documenting the entire process.
  • CA24 (Implement complete empirical projects to test hypotheses in official reports.) Implement complete empirical projects to test hypotheses in official reports.
  • KA35 (List international educational databases used in political economy.) List international educational databases used in political economy.
  • KA36 (Classify sources of bias in estimates of working hours.) Classify sources of bias in estimates of working hours.
  • KA37 (Recognise mechanisms of imitation in endogenous growth models.) Recognise mechanisms of imitation in endogenous growth models.
  • KA38 (Describe advanced microeconometric techniques for limited variables.) Describe advanced microeconometric techniques for limited variables.
  • KA39 (Explain health economics focused on incentives and costs.) Explain health economics focused on incentives and costs.
  • KA40 (Recognise political economy and models of collective choice.) Recognise political economy and models of collective choice.
  • SA32 (Select variables and data structures to build empirical models.) Select variables and data structures to build empirical models.
  • SA33 (Review the suitability of microeconometric methods for bias.) Review the suitability of microeconometric methods for bias.
  • SA34 (Organise applied policy questions within an econometric framework and solve them.) Organise applied policy questions within an econometric framework and solve them.

Contents


  • Data

  • Energy Economics

  • Industrial Economics and Energy

  • Macroeconometrics

  • Microeconometrics

  • Policy Evaluation

  • 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
Practical classes,learning based on problems sets, tutorials 25 1
Personal study, study groups, textbook readings, article readings 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
Class Attendance and Problem sets and assignments 22% 0 0 CA21, CA22, CA23, CA24, KA35, KA36, KA37, KA38, KA39, KA40, SA32, SA33, SA34
Midterm Exam 26% 0 0 CA21, CA22, CA23, CA24, KA35, KA36, KA37, KA38, KA39, KA40, SA32, SA33, SA34
Midterm Exam 26% 0 0 CA21, CA22, CA23, CA24, KA35, KA36, KA37, KA38, KA39, KA40, SA32, SA33, SA34
Midterm Exam 26% 0 0 CA21, CA22, CA23, CA24, KA35, KA36, KA37, KA38, KA39, KA40, SA32, SA33, SA34

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

Alesina, A., Giuliano, P. and Nunn, N.: 2013, On the origins of gender roles: Women and the plough, The Quarterly Journal of Economics 128(2), 469–530.

Amemiya, (1985), Advanced Econometrics, Blackwell

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

Bartik, T.J., Who Benefits from State and Local Economic Development Policies, Kalamazoo, MI: W.E. Upjohn Institute for Employment Research,1991.

Bartolucci, C. , F. Devicienti, and I. Monz_on, Identifying Sorting in Practice,\" American Economic Journal: Applied Economics,October 2018, 10 (4), 408{438.

Baskaran, T., Min, B. and Uppal, Y.: 2015, Election cycles and electricity provision: Evidence from a quasi-experiment with indian special elections, Journal of Public Economics 126, 64–73.

Becker, S. O. and Woessmann, L.: 2009, Was weber wrong? a human capital theory of protestant economic history, The Quarterly Journal of Economics 124(2), 531–596.

Berndt, Ernst R., B. H. Hall, R. E. Hall, and Jerry A. Hausman, \\Estimation and Inference in Nonlinear Structural Models,\" Annal of Economic and Social Measurement, October 1974, 3 (4), 653{666.

Black, S. E.: 1999, Do better schools matter? parental valuation of elementary education, The Quarterly Journal of Economics 114(2), 577–599.

Blundell, R. and S. Bond, \\Initial Conditions and Moment Restrictionsin Dynamic Panel Data Models,\" Journal of Econometrics, August 1998, 87 (1), 115{143. and , \\GMM Estimation with Persistent Panel Data: An Application to Production Functions,\" Econometric Reviews, March 2000, 19 (3), 321{340.

Bonhomme, S., T. Lamadon, and E. Manresa, \\A Distributional Framework for Matched Employer Employee Data,\" Econometrica, May 2019, 87 (3), 699{739.

Brockwell, P. J. and R. A. Davis, (2009), Time Series: Theory and Methods, Springer-Verlag: Berlin

Brodeur, A., Lekfuangfu, W. N. and Zylberberg, Y.: 2017, War, migration and the origins of the thai sex industry, Journal of the European Economic Association 16(5), 1540–1576.

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

Canova F. (2007), Methods for Applied Macroeconomic Research, Princeton University Press: Princeton

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

Hamilton J. D. (1994), Time Series Analysis, Princeton University Press: Princeton

Lutkepohl H. (2005), New Introduction to Multiple Time Series, Springer-Verlag: Berlin

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

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

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